Method, apparatus, and computer program for predicting consumer behavior

ABSTRACT

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting consumer behavior. In one embodiment a method is provided comprising determining a classification for a first consumer, wherein the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority to U.S.Provisional Application No. 62/212,758, titled “Method, Apparatus, andComputer Program Product for Predicting Consumer Behavior” filed Sep. 1,2015, the contents of which are hereby incorporated by reference intheir entirety.

BACKGROUND

A promotional and marketing service may utilize the internet to provideconsumers with available promotions related to products, services orexperiences offered by providers that may be of interest. Applicant hasidentified a number of deficiencies and problems associated withassessing and analyzing consumers. Through applied effort, ingenuity,and innovation, many of these identified problems have been solved bydeveloping solutions that are included in embodiments of the presentinvention, many examples of which are described in detail herein.

BRIEF SUMMARY

This specification relates to assessing and analyzing consumers based ona machine learning model.

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof receiving data associated with a first consumer of the plurality ofconsumers; determining, a classification for the first consumer, whereinthe classification is based on a measure of frequency of purchases bythe first consumer; identifying, one or more first attributes for thefirst consumer based on the determined classification, the one or moreattributes being attributes selected for predicting the respective oneor more metrics associated with the first consumer; and determining,based on values for the one or more first attributes, a first predictionvalue that indicates a programmatically expected number of futurepurchases by the first consumer.

Other embodiments of this aspect include corresponding systems,apparatus, and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

These and other embodiments can each optionally include one or more ofthe following features. Receiving data associated with a second consumerfrom the plurality of consumers, the second consumer being differentfrom the first consumer; determining, a classification for the secondconsumer, wherein the classification is based on a measure of frequencyof purchases by the first consumer, and wherein the classification ofthe second consumer is different from the classification of the firstconsumer; identifying, one or more second attributes for the secondconsumer based on the determined classification for the second consumer,the one or more second attributes being attributes selected forpredicting the respective one or more metrics associated with the secondconsumer, and wherein the classification of the second consumer isdifferent from the classification of the first consumer; Determining,based on values for the one or more second attributes, a secondprediction value that indicates a programmatically expected number offuture purchases by the second consumer, wherein the second predictionvalue is different from the first prediction value. Applying a decayfactor to the first prediction value, the first decay factor being afactor that proportionally reduces the first prediction value, whereinthe first decay factor is determined based on a measure of time lapsedbetween a time of the determination of the first prediction value and atime when the decay factor is applied; Receiving updated first valuesfor the one or more first attributes; determining that a first eventassociated with the first consumer occurred; in response to determiningthat the first event occurred, determining based on the updated firstvalues for the one or more first attributes, an updated prediction valuethat indicates an updated programmatically expected number of purchasesby the first consumer. Determining, based on values of third attributesdifferent from the first attributes, a third prediction specifyingwhether the first consumer accessing a computing device will make apurchase within a pre-specified time period, wherein the determining ofthe first prediction value is in response to determining that thirdprediction indicates that the consumer, using the computing device, willmake a purchase within the pre-specified time period. Delivering anadvertisement to the first consumer using a first computing device andnot providing an advertisement to the second consumer using a secondcomputing device, based, at least in part, on the first prediction andthe second prediction. Providing advertisements to the first consumerusing the first computing device at a different rate from providingadvertisements to the second consumer using the second computing device,based, at least in part, on the first prediction and the secondprediction.

In general, another aspect of the subject matter described in thisspecification can be embodied in methods that include the actions ofreceiving data for a first consumer of the plurality of consumers;determining, a classification for the first consumer, wherein theclassification is based on a measure of frequency of purchases by thefirst consumer; identifying, a set of attributes for the first consumerbased on the determined classification, the set of attributes beingattributes selected, for the classification of consumers, for predictingthe respective one or more metric associated with consumers classifiedwith the classification; selecting from the set of attributes a firstsubset of attributes and a second subset of attributes, wherein thefirst subset of attributes is different from the second subset ofattributes; determining, based on values for the first subset ofattributes, a first prediction value that indicates whether the firstconsumer will make a purchase within a pre-specified time period;determining, based on values for the second subset of attributes, asecond prediction value that indicates whether the first consumer willmake a purchase within a pre-specified time period; and determining, anoverall prediction based at least in part on the first and secondpredictions.

Other embodiments of this aspect include corresponding systems,apparatus, and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

In general, another aspect of the subject matter described in thisspecification can be embodied in methods that include the actions ofreceiving data associated with a first consumer of the plurality ofconsumers; determining, a classification for the first consumer, whereinthe classification is based on a measure of frequency of purchases bythe first consumer; identifying, a set of attributes for the firstconsumer based on the determined classification, the set of attributesbeing attributes selected, for the classification of consumers, forpredicting the respective one or more metric associated with consumersclassified with the classification; selecting from the set of attributesa first subset of attributes and a second subset of attributes, whereinthe first subset of attributes is different from the second subset ofattributes; determining, based on values for the first subset ofattributes, a first prediction value that indicates a programmaticallyexpected number of purchases by the first consumer; determining, basedon values for the second subset of attributes, a second prediction valuethat indicates a programmatically expected number of purchases by thefirst consumer; determining, an overall prediction based at least inpart on the first and second predictions.

Other embodiments of this aspect include corresponding systems,apparatus, and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. Provide accurate and efficient methods and systemsfor predicting purchases by consumers during a pre-specified period. Inturn, the predictions may be used to enhance, optimize and adjustmarketing and advertising efforts. Reduce the cost associated withmarketing and advertising efforts that are unlikely to result inpurchases and, in turn, increase the overall revenue. Reduce therequired processing power for servers maintaining and provisioning thepromotions. Similarly, reduction of the required processing powerincreases the overall revenue. Particular embodiments of the subjectmatter allow for providing consumers with highly relevant promotions andadvertisements at a rate that may yield a maximized number ofconversions and purchases. Particular embodiments of the subject matterfacilitate analyzing events that may affect consumer value, in order todetermine various metrics that can aid in optimizing business practicesand aid in product experimentation. Additionally, particular embodimentsof the subject matter allow for customized treatment of consumers basedon a predicted consumer value.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is an overview of an example system that can be used to practiceembodiments of the present invention;

FIG. 2 is an exemplary schematic diagram of a computing entity accordingto one embodiment of the present invention;

FIG. 3 is an exemplary data flow illustrating interactions between aserver, one or more consumer devices, and one or more merchant devices;

FIGS. 4-7, 9A-9B and 10A-10B are flow charts illustrating variousprocedures and operations that may be completed in accordance withvarious embodiments of the present invention;

FIG. 8A depicts an example graphical representation of a measure ofprediction accuracy against a configurable cutoff attribute fordifferent cohorts;

FIG. 8B shows a list of statistical data associated with differentcohorts.

FIGS. 11A-11D show a list of example attributes, for predicting consumerbehavior, and an associated ranking indicating a measure of importanceof each attribute for a plurality of cohorts;

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” is used herein in both the alternativeand conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout.

Overview

Various embodiments of the invention generally relate to predictingconsumer behavior. For example, the methods, apparatus and computerprogram products described herein are operable to determine whether aconsumer is likely to make a purchase and/or a number of purchases thatthe consumer is likely to make. A purchase may be, for example,accepting a promotion. Consumers are divided into different cohortsbased on historical data associated with the consumers. For example,consumers that make frequent purchases may belong to a cohort differentfrom a cohort to which consumers that make seldom purchases belong. Inone example, a different set of attributes may be used to predictconsumer behavior of consumers belonging to different cohorts.Similarly, in some implementations, a first set of attributes may beused to predict whether a particular consumer will make a purchasewithin a pre-specified time period, while a second set of attributes maybe used to predict the number of purchases that will be made by theparticular consumer.

Because analysis shows that different attributes have differentimportance in predicting consumer behavior for consumers belonging todifferent cohorts, a promotion and marketing service can access, captureand/or store data related to consumers belonging to different cohortsand utilize that information to determine likely return rates ofpromotions and advertisements.

In one example, a promotion and marketing system may supply a data setto a learning machine or algorithm. The learning machine or algorithmmay then determine which features or attributes of consumers belongingto different cohorts correlate to a number of purchases. Accordingly,the learning machine may select a subset of features or attributes fortraining associated with each cohort. Similarly, the learning machinemay select a subset of features or attributes for training associatedwith different predictions for a particular cohort. For example,different features or attributes may be used for training a systempredicting a number of purchases and for training a system forpredicating whether a purchase will be made within a pre-specified timeperiod. Once the learning machine or algorithm is trained, live dataassociated with other consumers may be input and the learning machine oralgorithm may then predict the behavior of the other consumers. As such,the promotion and marketing system may determine how to adjust, forexample, advertisement targeting and marketing efforts. Alternatively,or additionally, in some example embodiments, the learning machine oralgorithm may output a prediction specifying a number of purchaseslikely to be made from a particular promotion and marketing service orseller during a pre-specified period. This enables sellers to adjust,for example, their marketing strategy, number of employees, budgetsand/or the like.

Definitions

As used herein, the terms “data,” “content,” “information,” and similarterms may be used interchangeably to refer to data capable of beingtransmitted, received, and/or stored in accordance with embodiments ofthe present invention. Thus, use of any such terms should not be takento limit the spirit and scope of embodiments of the present invention.Further, where a computing device is described herein to receive datafrom another computing device, it will be appreciated that the data maybe received directly from another computing device or may be receivedindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a computing device is described herein tosend data to another computing device, it will be appreciated that thedata may be sent directly to another computing device or may be sentindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like.

As used herein, the term “promotion and marketing service” may include aservice that is accessible via one or more computing devices and that isoperable to provide promotion and/or marketing services on behalf of oneor more providers that are offering one or more instruments that areredeemable for goods, services, experiences and/or the like. In someexamples, the promotion and marketing service may take the form of aredemption authority, a payment processor, a rewards provider, an entityin a financial network, a promoter, an agent and/or the like. As such,the service is, in some example embodiments, configured to present oneor more promotions via one or more impressions, accept payments forpromotions from consumers, issue instruments upon acceptance of an offeror a promotion, participate in redemption, generate rewards, provide apoint of sale device or service, issue payments to providers and/orotherwise participate in the exchange of goods, services or experiencesfor currency, value and/or the like. The service is also, in someexample embodiments, configured to offer merchant services such aspromotion building (e.g., assisting merchants with selecting parametersfor newly created promotions), promotion counseling (e.g., offeringinformation to merchants to assist with using promotions as marketing),promotion analytics (e.g., offering information to merchants to providedata and analysis regarding the costs and return-on-investmentassociated with offering promotions), and the like.

As used herein, the terms “provider” and “merchant” may be usedinterchangeably and may include, but are not limited to, a businessowner, consigner, shopkeeper, tradesperson, vendor, operator,entrepreneur, agent, dealer, organization or the like that is in thebusiness of providing a good, service or experience to a consumer,facilitating the provision of a good service or experience to a consumerand/or otherwise operating in the stream of commerce. The “provider” or“merchant” need not actually market a product or service via thepromotion and marketing service, as some merchants or providers mayutilize the promotion and marketing service only for the purpose ofgathering marketing information, demographic information, or the like.

As used herein, the term “consumer” should be understood to refer to arecipient of goods, services, promotions, media, or the like provided bythe promotion and marketing service and/or a merchant. Consumers mayinclude, without limitation, individuals, groups of individuals,corporations, other merchants, and the like.

As used herein, the term “promotion” may include, but is not limited to,any type of offered, presented or otherwise indicated reward, discount,coupon, credit, deal, incentive, discount, media or the like that isindicative of a promotional value or the like that upon purchase oracceptance results in the issuance of an instrument that may be usedtoward at least a portion of the purchase of particular goods, servicesand/or experiences defined by the promotion. Promotions may havedifferent values in different contexts. For example, a promotion mayhave a first value associated with the cost paid by a consumer, known asan “accepted value.” When redeemed, the promotion may be used topurchase a “promotional value” representing the retail price of thegoods. The promotion may also have a “residual value,” reflecting theremaining value of the promotion after expiration. Although consumersmay be primarily focused on the accepted and promotional value of thepromotion, a promotion may also have additional associated values. Forexample, a “cost value” may represent the cost to the merchant to offerthe promotion via the promotion and marketing service, where thepromotion and marketing service receives the cost value for eachpromotion sold to a consumer. The promotion may also include a “returnon investment” value, representing a quantified expected return oninvestment to the merchant for each promotion sold.

For example, consider a promotion offered by the promotion and marketingservice for a $50 meal promotion for $25 at a particular restaurant. Inthis example, $25 would be the accepted value charged to the consumer.The consumer would then be able to redeem the promotion at therestaurant for $50 applied toward their meal check. This $50 would bethe promotional value of the promotion. If the consumer did not use thepromotion before expiration, the consumer might be able to obtain arefund of $22.50, representing a 10% fee to recoup transaction costs forthe merchant and/or promotion and marketing service. This $22.50 wouldbe the residual value of the promotion. If the promotion and marketingservice charged the merchant $3.00 to offer the promotion, the $3.00 feewould be the “cost value.” The “return on investment” value of thepromotion might be dynamically calculated by the promotion and marketingservice based on the expected repeat business generated by the marketingof the promotion, the particular location, the demographics of theconsumer, and the like. For example, the return on investment valuemight be $10.00, reflecting the long term additional profit expected bythe merchant as a result of bringing in a new customer through use of apromotion.

Promotions may be provided to consumers and redeemed via the use of an“instrument.” Instruments may represent and embody the terms of thepromotion from which the instrument resulted. For example, instrumentsmay include, but are not limited to, any type of physical token (e.g.,magnetic strip cards or printed barcodes), virtual account balance(e.g., a promotion being associated with a particular user account on amerchant website), secret code (e.g., a character string that can beentered on a merchant website or point-of-sale), tender, electroniccertificate, medium of exchange, voucher, or the like, which may be usedin a transaction for at least a portion of the purchase, acquisition,procurement, consumption or the like of goods, services and/orexperiences as defined by the terms of the promotion.

In some examples, the instrument may take the form of tender that has agiven value that is exchangeable for goods, services and/or experiencesand/or a reduction in a purchase price of a particular good, service orexperience. In some examples, the instrument may have multiple values,such as accepted value, a promotional value and/or a residual value. Forexample, using the aforementioned restaurant as the example provider, anelectronic indication in a mobile application that shows $50 of value tobe used as payment for a meal check at the restaurant. In some examples,the accepted value of the instrument is defined by the value exchangedfor the instrument. In some examples, the promotional value is definedby the promotion from which the instrument resulted and is the value ofthe instrument beyond the accepted value. In some examples, the residualvalue is the value after redemption, the value after the expiry or otherviolation of a redemption parameter, the return or exchange value of theinstrument and/or the like.

As used herein, the term “redemption” refers to the use, exchange orother presentation of an instrument for at least a portion of a good,service or experience as defined by the instrument and its relatedpromotion. In some examples, redemption includes the verification ofvalidity of the instrument. In other example embodiments, redemption mayinclude an indication that a particular instrument has been redeemed andthus no longer retains an actual, promotional and/or residual value(e.g., full redemption). In other example embodiments, redemption mayinclude the redemption of at least a portion of its actual, promotionaland/or residual value (e.g., partial redemption). An example ofredemption, using the aforementioned restaurant as the example provider,is the exchange of the $50 instrument and $50 to settle a $100 mealcheck.

As used herein, the term “impression” refers to a metric for measuringhow frequently consumers are provided with marketing information relatedto a particular good, service, or promotion. Impressions may be measuredin various different manners, including, but not limited to, measuringthe frequency with which content is served to a consumer (e.g., thenumber of times images, websites, or the like are requested byconsumers), measuring the frequency with which electronic marketingcommunications including particular content are sent to consumers (e.g.,a number of e-mails sent to consumers or number of e-mails includingparticular promotion content), measuring the frequency with whichelectronic marketing communications are received by consumers (e.g., anumber of times a particular e-mail is read), or the like. Impressionsmay be provided through various forms of media, including but notlimited to communications, displays, or other perceived indications,such as e-mails, text messages, application alerts, mobile applications,other types of electronic interface or distribution channels and/or thelike, of one or more promotions.

As used herein, the term “electronic marketing information” refers tovarious electronic data and signals that may be interpreted by apromotion and marketing service to provide improved electronic marketingcommunications. Electronic marketing information may include, withoutlimitation, clickstream data (defined below), transaction data (definedbelow), location data (defined below), communication channel data(defined below), discretionary data (defined below), or any other datastored by or received by the promotion and marketing service for use inproviding electronic communications to consumers.

As used herein, the term “clickstream data” refers to electronicinformation indicating content viewed, accessed, edited, or retrieved byconsumers. This information may be electronically processed and analyzedby a promotion and marketing service to improve the quality ofelectronic marketing and commerce transactions offered by, through, andin conjunction with the promotion and marketing service. It should beunderstood that the term “clickstream” is not intended to be limited tomouse clicks. For example, the clickstream data may include variousother consumer interactions, including without limitation, mouse-overevents and durations, the amount of time spent by the consumer viewingparticular content, the rate at which impressions of particular contentresult in sales associated with that content, demographic informationassociated with each particular consumer, data indicating other contentaccessed by the consumer (e.g., browser cookie data), the time or dateon which content was accessed, the frequency of impressions forparticular content, associations between particular consumers orconsumer demographics and particular impressions, and/or the like.

As used herein, the term “transaction data” refers to electronicinformation indicating that a transaction is occurring or has occurredvia either a merchant or the promotion and marketing service.Transaction data may also include information relating to thetransaction. For example, transaction data may include consumer paymentor billing information, consumer shipping information, items purchasedby the consumer, a merchant rewards account number associated with theconsumer, the type of shipping selected by the consumer for fulfillmentof the transaction, or the like.

As used herein, the term “location data” refers to electronicinformation indicating a particular location. Location data may beassociated with a consumer, a merchant, or any other entity capable ofinteraction with the promotion and marketing service. For example, insome embodiments location data is provided by a location services moduleof a consumer mobile device. In some embodiments, location data may beprovided by a merchant indicating the location of consumers within theirretail location. In some embodiments, location data may be provided bymerchants to indicate the current location of the merchant (e.g., a foodtruck or delivery service). It should be appreciated that location datamay be provided by various systems capable of determining locationinformation, including, but not limited to, global positioning servicereceivers, indoor navigation systems, cellular tower triangulationtechniques, video surveillance systems, or radio frequencyidentification (RFID) location systems. Throughout this specification,the terms “user device” and “consumer device” may be usedinterchangeably.

As used herein, the term “communication channel data” refers toelectronic information relating to the particular device orcommunication channel upon which a merchant or consumer communicateswith the promotion and marketing service. In this regard, communicationchannel data may include the type of device used by the consumer ormerchant (e.g., smart phone, desktop computer, laptop, netbook, tabletcomputer), the Internet Protocol (IP) address of the device, theavailable bandwidth of a connection, login credentials used to accessthe channel (e.g., a user account and/or password for accessing thepromotion and marketing service), or any other data pertaining to thecommunication channel between the promotion and marketing service and anentity external to the promotion and marketing service.

As used herein, the term “discretionary data” refers to electronicinformation provided by a merchant or consumer explicitly to thepromotion and marketing service in support of improved interaction withthe promotion and marketing service. Upon registering with the promotionand marketing service or at any time thereafter, the consumer ormerchant may be invited to provide information that aids the promotionand marketing service in providing services that are targeted to theparticular needs of the consumer or merchant. For example, a consumermay indicate interests, hobbies, their age, gender, or location whencreating a new account. A merchant may indicate the type of goods orservices provided, their retail storefront location, contactinformation, hours of operation, or the like.

It should be appreciated that the term “discretionary data” is intendedto refer to information voluntarily and explicitly provided to thepromotion and marketing service, such as by completing a form or surveyon a website or application hosted by the promotion and marketingservice. However, it should be appreciated that the examples ofdiscretionary data provided above may also be determined implicitly orthrough review or analysis of other electronic marketing informationprovided to the promotion and marketing service. It should also beappreciated that the promotion and marketing service may also grantaccess to certain features or tools based on whether certaindiscretionary data has been provided. For example, the consumer may berequired to provide information relating to their interests or locationduring a registration process. In some implementations, the“discretionary data” may include demographic data.

As used herein, the term “offering parameters” refers to terms andconditions under which the promotion is offered by a promotion andmarketing service to consumers. These offering parameters may includeparameters, bounds, considerations and/or the like that outline orotherwise define the terms, timing, constraints, limitations, rules orthe like under which the promotion is sold, offered, marketed, orotherwise provided to consumers. Example offering parameters include,using the aforementioned restaurant as the example provider, limited toone instrument per person, total of 100 instruments to be issued, a runduration of when the promotion will be marketed via the promotion andmarketing service, and parameters for identifying consumers to beoffered the promotion (e.g., factors influencing how consumer locationsare used to offer a promotion).

As used herein, the term “redemption parameters” refers to terms andconditions for redeeming or otherwise obtaining the benefit ofpromotions obtained from a promotion and marketing service. Theredemption parameters may include parameters, bounds, considerationsand/or the like that outline the term, timing, constraints, limitations,rules or the like for how and/or when an instrument may be redeemed. Forexample, the redemption parameters may include an indication that theinstrument must be redeemed prior to a specified deadline, for aspecific good, service or experience and/or the like. For example, usingthe aforementioned restaurant as the example provider, the redemptionparameters may specify a limit of one instrument per visit, that thepromotion must be used in-store only, or that the promotion must be usedby a certain date.

As used herein, the term “promotion content” refers to display factorsor features that influence how the promotion is displayed to consumers.For example, promotion content may include an image associated with thepromotion, a narrative description of the promotion or the merchant, adisplay template for association with the promotion, or the like. Forexample, merchant self-service indicators (defined below) may be used toidentify promotion offers that were generated by merchants with similarcharacteristics to the merchant self-service indicators. Various otherfactors may be used to generate the promotion offer, such as the successof the promotion offers generated by the merchants with similarcharacteristics, the product availability of the merchant, and the like.

As used herein, the term “promotion component” is used to refer toelements of a particular promotion that may be selected during apromotion generation process. Promotion components may include anyaspect of a promotion, including, but not necessarily limited to,offering parameters, redemption parameters, and promotion content. Forexample, promotion components may include, but are not limited to,promotion titles, promotion ledes (e.g., a short text phrase displayedunder a promotion title), promotion images, promotion prices, promotiondiscount levels, promotion style sheets, promotion fonts, promotione-mail subjects, promotion quantities, promotion fine print options,promotion fees assessed to the merchant by the promotion and marketingservice, or the like. Promotion components may also include variousflags and settings associated with registration and verificationfunctions for a merchant offering the promotion, such as whether theidentity of the merchant has been verified, whether the merchant isregistered with the promotion and marketing service, or the like.

As used herein, the term “electronic marketing communication” refers toany electronically generated information content provided by thepromotion and marketing service to a consumer for the purpose ofmarketing a promotion, good, or service to the consumer. Electronicmarketing communications may include any email, short message service(SMS) message, web page, application interface, or the like,electronically generated for the purpose of attempting to sell or raiseawareness of a product, service, promotion, or merchant to the consumer.

It should be appreciated that the term “electronic marketingcommunication” implies and requires some portion of the content of thecommunication to be generated via an electronic process. For example, atelephone call made from an employee of the promotion and marketingservice to a consumer for the purpose of selling a product or servicewould not qualify as an electronic marketing communication, even if theidentity of the call recipient was selected by an electronic process andthe call was dialed electronically, as the content of the telephone callis not generated in an electronic manner. However, a so-called“robo-call” with content programmatically selected, generated, orrecorded via an electronic process and initiated by an electronic systemto notify a consumer of a particular product, service, or promotionwould qualify as an electronic marketing communication. Similarly, amanually drafted e-mail sent from an employee of the promotion andmarketing service to a consumer for the purpose of marketing a productwould not qualify as an electronic marketing communication. However, aprogrammatically generated email including marketing materialsprogrammatically selected based on electronic marketing informationassociated with the recipient would qualify as an electronic marketingcommunication.

As used herein, the term “business analytic data” refers to datagenerated by the promotion and marketing service based on electronicmarketing information to assist with the operation of the promotion andmarketing service and/or one or more merchants. The various streams ofelectronic marketing information provided to and by the promotion andmarketing service allow for the use of sophisticated data analysistechniques that may be employed to identify correlations, relationships,and other associations among elements of electronic marketinginformation. These associations may be processed and formatted by thepromotion and marketing service to provide reports, recommendations, andservices both internal to the promotion and marketing service and tomerchants in order to improve the process by which merchants andpromotion and marketing service engage with consumers. For example, thepromotion and marketing service may analyze the electronic marketinginformation to identify an increased demand for a particular product orservice, and provide an electronic report to a merchant suggesting themerchant offer the particular product or service. Alternatively, thepromotion and marketing service may identify that a particular productor service is not selling well or that sales of the product or serviceresult in the merchant losing money, customers, or market share (e.g.,after consumers order a particular menu item, they never come back tothe merchant), and suggest that the merchant should discontinue offeringthat product or service.

It should be appreciated that the term “business analytic data” isintended to refer to electronically and programmatically generated data.For example, a printed report or letter manually drafted by an employeeof the promotion and marketing service would not be said to includebusiness analytic data, even if said data was used by the employeeduring the drafting process, while a data disk or downloaded filecontaining analytics generated by the promotion and marketing servicewould be considered business analytic data.

As used herein, the terms “merchant self-service indicator” and“promotion context” relate to data associated with the merchant that maybe used to classify the merchant or suggest promotion components to themerchant. A promotion context may include a plurality of merchantself-service indicators. For example, a promotion context may includemultiple merchant self-service indicators that describe various featuresor characteristics of the merchant, such as a the type of industry ofthe merchant, the type of products or services sold by the merchant, thesize of the merchant, the location of the merchant, the sales volume ofthe merchant, reviews and ratings for the merchant, or the like.

In some embodiments, the merchant self-service indicators are a resultof analytics that allow for generation of promotions that are ideal forthe particular merchant's circumstances. For example, the merchantself-service indicators may be used to identify optimal promotions forthe particular merchant based on their exact location (e.g., theparticular city street of the merchant as opposed to a wider range, suchas a zip code), the merchant's exact products and services offered(e.g., pizzerias that only serve deep dish pizza, restaurants thatbecome nightclubs after 11:00 pm), the merchant's price point (e.g.,barbershops that charge more than $20 for a haircut), or the date orseason of the year (e.g., offering ski equipment during the winter, orholiday themed promotions during the holiday season), or the like. Thesemerchant self-service indicators may be used in a self-service processto identify promotion components that were used by other merchants thatshare one or more same or similar merchant self-service indicators. Forexample, after initial registration and verification, the promotion andmarketing service may identify the merchant self-service indicatorsassociated with the newly registered merchant, such as by looking up themerchant in a merchant database or by receiving the merchantself-service indicators directly from the merchant (e.g., by a fillableform). The merchant self-service indicators and promotion contexts maybe used for classification of merchants. For example, such attributesmay be used to identify whether a promotion for a specific merchant islikely to satisfy consumers (e.g., the promotion has 70% chance tosatisfy consumers. Further, predictions of consumer behavior may be usedin combination with the merchant attributes above to identify whether apromotion for a specific merchant is likely to satisfy a particularconsumer belonging to a particular cohort. For example, a promotion mayhave an 85% chance to satisfy a first consumer, while having a 20%chance to satisfy a second consumer. Accordingly, the optimal promotionsfor the particular merchant may be provided to consumers that are likelyto be satisfied with the optimal promotion. For example, the promotionmay be provided to the first consumer and not provided to the secondconsumer.

It should be appreciated that the term “programmatically expected”indicates machine prediction of occurrence of certain events. Forexample, a “programmatically expected” number of purchases by a firstconsumer is a number determined by machine prediction specifying theexpected number of promotions that will be purchased by the firstconsumer.

As used herein, the term “likelihood” refers to a measure of probabilityfor occurrence of a particular event. For example, the likelihood that aconsumer will purchase a promotion within a pre-specified period may bea value associated with a specific scale. In some implementations, themachine predictions discussed above are based, at least in part, on the“likelihood” that an event will occur. Similarly, in someimplementations, machine predictions are based on attributes associatedwith a consumer and/or an associated merchant promotion.

It should be appreciated that the terms “subset” describes a propersubset. A proper subset of set is portion of the set that is not equalto the set. For example, if elements A, B, and C belong to a first set,a subset including elements A and B is a proper subset of the first set.However, a subset including elements A, B, and C is not a proper subsetof the first set.

As used herein the term “cohort” refers to group or sub group that hasone or more common features. For example, consumers that have only madea single purchase in a previous year may belong to a particular cohort.Similarly, consumers that made 10 purchases in the same year may belongto different cohort.

Technical Underpinnings and Implementation of Exemplary Embodiments

Merchants, including manufacturers, wholesalers, and retailers, havespent a tremendous amount of time, money, manpower, and other resourcesto determine the best way to market their products to consumers. Whethera given marketing effort is successful is often determined based on thereturn-on-investment offered to the merchant from increased awareness,sales, and the like, of the merchant's goods and services in exchangefor the resources spent on the marketing effort. In other words, optimalmarketing techniques generally maximize the benefit to the merchant'sbottom line while minimizing the cost spent on marketing. To this end, amerchant's marketing budget may be spent in a variety of differentmanners including advertising, offering of discounts, conducting marketresearch, and various other known marketing techniques. The end goal ofthese activities is to ensure that products are presented to consumersin a manner that maximizes the likelihood that the consumers willpurchase the product from the merchant that performed the marketingactivities while minimizing the expense of the marketing effort.

The advent of electronic commerce has revolutionized the marketingprocess. While merchants would typically have to perform costly marketresearch such as focus groups, surveys, and the like to obtain detailedinformation on consumer preferences and demographics, the digital agehas provided a wealth of new consumer information that may be used tooptimize the marketing and sales process. As a result, new technologieshave been developed to gather, aggregate, analyze, and reportinformation from a variety of electronic sources.

So-called “clickstream data” provides a robust set of informationdescribing the various interactions consumers have with electronicmarketing information provided to them by merchants and others.Promotion and marketing services have been developed with sophisticatedtechnology to receive and process this data for the benefit of bothmerchants and consumers. These services assist merchants with marketingtheir products to interested consumers, while reducing the chance that aconsumer will be presented with marketing information in which theconsumer has no interest. Some promotion and marketing services furtherleverage their access to the trove of electronic marketing informationto assist merchants and consumers with other tasks, such as offeringimproved merchant point-of-sale systems, improved inventory and supplychain management, improved methods for delivering products and services,and the like.

Unlike conventional marketing techniques related to the use of paper orother physical media (e.g., coupons clipped from a weekly newspaper),promotion and marketing services offer a wealth of additional electronicsolutions to improve the experience for consumers and merchants. Theability to closely monitor user impressions provides the ability for thepromotion and marketing service to gather data related to the time,place, and manner in which the consumer engaged with the impression(e.g., viewed, clicked, moused-over) and obtained and redeemed thepromotion. The promotion and marketing service may use this informationto determine which products and services are most relevant to theconsumer's interest, and to provide marketing materials related to saidproducts and services to the consumer, thus improving the quality of theelectronic marketing communications received by the consumer. Merchantsmay be provided with the ability to dynamically monitor and adjust theparameters of promotions offered by the promotion and marketing service,ensuring that the merchant receives a positive return on theirinvestment. For example, the merchant can closely monitor the type,discount level, and quantity sold of a particular promotion on the fly,while with traditional printed coupons the merchant would not be able tomake any changes to the promotion after the coupon has gone to print.Each of these advancements in digital market and promotion distributioninvolve problems unique to the digital environment not before seen intraditional print or television broadcast marketing.

However, these promotion and marketing services are not withoutproblems. Although the clickstream data provides a wealth ofinformation, the inventors have determined that existing techniques maynot always leverage this information in an efficient or accurate manner.Technology continues to rapidly advance in the field of analytics andthe processing of this information, offering improved data gathering andanalysis techniques, resulting in more relevant and accurate resultsprovided in a more efficient manner. Electronic marketing servicescontinue to evolve and provide improved methods for engaging consumersand spreading awareness of products offered by promotion and marketingservices.

In many cases, the inventors have determined that these services areconstrained by technological obstacles unique to the electronic natureof the services provided, such as constraints on data storage, accuracyof data available, machine communication and processor resources. Theinventors have identified that the wealth of electronic data availableto these services and the robust nature of electronic marketingcommunications techniques present new challenges never contemplated inthe world of paper coupons and physical marketing techniques. Theinventors have further determined that even technological methods thatleverage computers for statistical analysis and consumer behaviormodeling (e.g., television rating systems) fail to address problemsassociated with providing relevant, high quality electronic marketingcommunications (e.g., impressions) to consumers in a manner thatmaximizes accuracy, minimizes error, is user friendly and provides forefficient allocation of resources. Embodiments of the present inventionas described herein serve to correct these errors and offer improvedresource utilization, thus providing improvements to electronicmarketing services that address problems arising out of the electronicnature of those services. For example, providing promotions andadvertisements to consumers based on predictions of consumer behaviorand at a rate based on the prediction, ensures that the consumers areresponsive to the promotions and enhances the consumer experience.Similarly, advertisement and marketing efforts may target consumers thatare likely to be satisfied with and be responsive to advertisements andmarketing efforts. In turn, this reduces resources required to manageand provide promotions. Similarly, this reduces the cost associated withproviding promotions, advertisements and cost associated with marketingcampaigns. By eliminating the processing required for providingpromotions, advertisements, and conducting advertisement campaigns thatare unlikely to be successful, the stress on processor 202 issubstantially reduced. For example, since the numbers of operationsperformed by processor 202 are significantly reduced, the powerconsumption, and the processing power and speed requirements forprocesser 202 are also reduced. In turn the maintained and operationalcosts of circuitry 200 are also reduced.

The inventors have identified that being able to more accurately predictconsumer behavior, can greatly enhance the performance of promotionaland marketing services. Accordingly being able to accurately predictconsumers' behavior can be very useful in selecting promotions to offereach respective consumer. Similarly, accurate predictions of consumerbehavior can be used to determine how to most effectively provideadvertisements to consumers. For example, the predictions may be used todetermine the most effective medium of advertising for a particularconsumer, the most effective frequency of providing advertisements toconsumers, and/or other optimizations for marketing campaigns. Thepredictions may be used to determine financial projections associatedwith the promotional and marketing service. Similarly, the predictionsmay be used to optimize financial aspects associated with thepromotional and marketing service. For example, the predictions can aidin determining the most effective size of inventory, the most effectivenumber of employees at different departments, and/or the like.

Being able to selectively offer a promotion and advertisements toconsumers, based on accurate consumer behavior predictions, allows foroffering of higher quality promotions that are likely to satisfyconsumers and result in conversions. This, in turn, is reflected on theresources required to operate and maintain the promotional service. Forexample, providing promotions that are likely to satisfy consumers,increases the processing efficiency of exemplary circuitry 200 andreduces the stress on exemplary circuitry 200. In some implementations,this can result in faster processing and response times, which in turnimprove the consumer experience. Additionally, the consumer experienceis also enhanced because the consumer is less likely to receivepromotions that are not relevant to their interests.

The inventors have also identified multiple obstacles associated withaccurately predicting consumer behavior. Generally, a machine learnedmodel is trained with a random sample of data. However, the inventorsrealized that predictions particular segments of consumers are likely tobe accurate when based on similar attributes. Additionally, trainingprediction models based on a large random sample of data may cause ahigh level of stress on the performing system. Accordingly, theinventors realized that training cohorts of consumers that share acertain measure of similarity independently will yield more accurateresults and reduce the stress on the performing system. Similarly, theinventors realized that most effective attributes for determiningwhether a consumer will make a purchase are different than attributesthat are most effective in determining the number of purchases that aconsumer is likely to purchase. Therefore, the inventors determined thata 2 stage prediction system that is trained based on differentattributes for different cohorts would not only increase the accuracy ofpredictions, but also reduce the required processing power of theperforming system. In turn, this reduces the power consumption,maintenance costs and heat generation by the performing system.

The inventors have therefore determined that existing electronic systemsfor predicting consumer behavior fail to accurately and efficientlyaddress these issues. As a result of these problems and others that mayarise from time to time, delays and inefficiencies may be introducedinto the prediction process, which in turn may be reflected on usersatisfaction and overall revenue generated. The inventors identified aset of attributes that when incorporated in a machine prediction systemcan accurately and efficiently predict whether a consumer will make apurchase (e.g., accept a promotion) within a pre-specified window, andthe number of purchases that are likely to be made by the consumer. Insome implementations, the attributes are different for different cohortsand/or groups of consumers. As a result, the predictions and above maybe utilized to significantly improve the user experience, allow foroptimization of various performance aspects associated with thepromotional and advertisement service, and increase the overall revenueassociated with offering promotions.

System Architecture and Example Apparatus

Methods, apparatuses, and computer program products of the presentinvention may be embodied by any of a variety of devices. For example,the method, apparatus, and computer program product of an exampleembodiment may be embodied by a networked device, such as a server orother network entity, configured to communicate with one or moredevices, such as one or more client devices. Additionally oralternatively, the computing device may include fixed computing devices,such as a personal computer or a computer workstation. Still further,example embodiments may be embodied by any of a variety of mobileterminals, such as a portable digital assistant (PDA), mobile telephone,smartphone, laptop computer, tablet computer, or any combination of theaforementioned devices.

In this regard, FIG. 1 discloses an example computing system withinwhich embodiments of the present invention may operate. Merchants mayaccess a promotion and marketing service 102 via a network 112 (e.g.,the Internet, or the like) using computer devices 108A through 108N and110A through 110N, respectively (e.g., one or more consumer devices108A-108N or one or more merchant devices 110A-110N). Moreover, thepromotion and marketing service 102 may comprise a server 104 incommunication with a database 106.

The server 104 may be embodied as a computer or computers as known inthe art. The server 104 may provide for receiving of electronic datafrom various sources, including but not necessarily limited to theconsumer devices 108A-108N and the merchant devices 110A-110N. Forexample, the server 104 may be operable to receive and processclickstream data provided by the consumer devices 108 and/or themerchant devices 110. The server 104 may also facilitate e-commercetransactions based on transaction information provided by the consumerdevices 108 and/or the merchant devices 110. The server 104 mayfacilitate the generation and providing of various electroniccommunications and marketing materials based on the received electronicdata.

The database 106 may be embodied as a data storage device such as aNetwork Attached Storage (NAS) device or devices, or as a separatedatabase server or servers. The database 106 includes informationaccessed and stored by the server 104 to facilitate the operations ofthe promotion and marketing service 102. For example, the database 106may include, without limitation, user account credentials for systemadministrators, merchants, and consumers, data indicating the productsand promotions offered by the promotion and marketing service,clickstream data, analytic results, reports, financial data, and/or thelike.

The consumer devices 108A-108N may be any computing device as known inthe art and operated by a consumer. Electronic data received by theserver 104 from the consumer devices 108A-108N may be provided invarious forms and via various methods. For example, the consumer devices108A-108N may include desktop computers, laptop computers, smartphones,netbooks, tablet computers, wearables, and the like. The information maybe provided through various sources on these consumer devices.

In embodiments where a consumer device 108 is a mobile device, such as asmart phone or tablet, the consumer device 108 may execute an “app” tointeract with the promotion and marketing service 102. Such apps aretypically designed to execute on mobile devices, such as tablets orsmartphones. For example, an app may be provided that executes on mobiledevice operating systems such as Apple Inc.'s iOS®, Google Inc.'sAndroid®, or Microsoft Inc.'s Windows 8®. These platforms typicallyprovide frameworks that allow apps to communicate with one another andwith particular hardware and software components of mobile devices. Forexample, the mobile operating systems named above each provideframeworks for interacting with location services circuitry, wired andwireless network interfaces, user contacts, and other applications in amanner that allows for improved interactions between apps while alsopreserving the privacy and security of consumers. In some embodiments, amobile operating system may also provide for improved communicationinterfaces for interacting with external devices (e.g., home automationsystems, indoor navigation systems, and the like). Communication withhardware and software modules executing outside of the app is typicallyprovided via application programming interfaces (APIs) provided by themobile device operating system.

The promotion and marketing service 102 may leverage the applicationframework offered by the mobile operating system to allow consumers todesignate which information is provided to the app and which may then beprovided to the promotion and marketing service 102. In someembodiments, consumers may “opt in” to provide particular data to thepromotion and marketing service 102 in exchange for a benefit, such asimproved relevancy of marketing communications offered to the user. Insome embodiments, the consumer may be provided with privacy informationand other terms and conditions related to the information provided tothe promotion and marketing service 102 during installation or use ofthe app. Once the consumer provides access to a particular feature ofthe mobile device, information derived from that feature may be providedto the promotion and marketing service 102 to improve the quality of theconsumer's interactions with the promotion and marketing service.

For example, the consumer may indicate that they wish to providelocation information to the app from location services circuitryincluded in their mobile device. Providing this information to thepromotion and marketing service 102 may enable the promotion andmarketing service 102 to offer promotions to the consumer that arerelevant to the particular location of the consumer (e.g., by providingpromotions for merchants proximate to the consumer's current location).It should be appreciated that the various mobile device operatingsystems may provide the ability to regulate the information provided tothe app associated with the promotion and marketing service 102. Forexample, the consumer may decide at a later point to disable the abilityof the app to access the location services circuitry, thus limiting theaccess of the consumer's location information to the promotion andmarketing service 102.

Various other types of information may also be provided in conjunctionwith an app executing on the consumer's mobile device. For example, ifthe mobile device includes a social networking feature, the consumer mayenable the app to provide updates to the consumer's social network tonotify friends of a particularly interesting promotion. It should beappreciated that the use of mobile technology and associated appframeworks may provide for particularly unique and beneficial uses ofthe promotion and marketing service through leveraging the functionalityoffered by the various mobile operating systems.

Additionally or alternatively, the consumer device 108 may interactthrough the promotion and marketing service 102 via a web browser. Asyet another example, the consumer device 108 may include varioushardware or firmware designed to interface with the promotion andmarketing service 102 (e.g., where the consumer device 108 is apurpose-built device offered for the primary purpose of communicatingwith the promotion and marketing service 102, such as a store kiosk).

The merchant devices 110A-110N may be any computing device as known inthe art and operated by a merchant. For example, the merchant devices110A-110N may include a merchant point-of-sale, a merchant e-commerceserver, a merchant inventory system, or a computing device accessing aweb site designed to provide merchant access (e.g., by accessing a webpage via a browser using a set of merchant account credentials).Electronic data received by the promotion and marketing service 102 fromthe merchant devices 110A-110N may also be provided in various forms andvia various methods. For example, the merchant devices 110A-110N mayprovide real-time transaction and/or inventory information as purchasesare made from the merchant. In other embodiments, the merchant devices110A-110N may be employed to provide information to the promotion andmarketing service 102 to enable the promotion and marketing service 102to generate promotions or other marketing information to be provided toconsumers.

An example of a data flow for exchanging electronic information amongone or more consumer devices, merchant devices, and the promotion andmarketing service is described below with respect to FIG. 3.

Example Apparatus for Implementing Embodiments of the Present Invention

The server 104 may be embodied by one or more computing systems, such asapparatus 200 shown in FIG. 2. As illustrated in FIG. 2, the apparatus200 may include a processor 202, a memory 204, input/output circuitry206, communications circuitry 208, cohort management circuitry 210,binary promotion prediction circuitry 212, and number of promotionsprediction circuitry 214. The apparatus 200 may be configured to executethe operations described above with respect to FIG. 1 and below withrespect to FIGS. 3-7, and 9-10. Although these components 202-214 aredescribed with respect to functional limitations, it should beunderstood that the particular implementations necessarily include theuse of particular hardware. It should also be understood that certain ofthese components 202-214 may include similar or common hardware. Forexample, two sets of circuitry may both leverage use of the sameprocessor, network interface, storage medium, or the like to performtheir associated functions, such that duplicate hardware is not requiredfor each set of circuitry. The use of the term “circuitry” as usedherein with respect to components of the apparatus should therefore beunderstood to include particular hardware configured to perform thefunctions associated with the particular circuitry as described herein.

The term “circuitry” should be understood broadly to include hardwareand, in some embodiments, software for configuring the hardware. Forexample, in some embodiments, “circuitry” may include processingcircuitry, storage media, network interfaces, input/output devices, andthe like. In some embodiments, other elements of the apparatus 200 mayprovide or supplement the functionality of particular circuitry. Forexample, the processor 202 may provide processing functionality, thememory 204 may provide storage functionality, the communicationscircuitry 208 may provide network interface functionality, and the like.

In some embodiments, the processor 202 (and/or co-processor or any otherprocessing circuitry assisting or otherwise associated with theprocessor) may be in communication with the memory 204 via a bus forpassing information among components of the apparatus. The memory 204may be non-transitory and may include, for example, one or more volatileand/or non-volatile memories. In other words, for example, the memorymay be an electronic storage device (e.g., a computer readable storagemedium). The memory 204 may be configured to store information, data,content, applications, instructions, or the like, for enabling theapparatus to carry out various functions in accordance with exampleembodiments of the present invention.

The processor 202 may be embodied in a number of different ways and may,for example, include one or more processing devices configured toperform independently. Additionally or alternatively, the processor mayinclude one or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining, and/ormultithreading. The use of the term “processing circuitry” may beunderstood to include a single core processor, a multi-core processor,multiple processors internal to the apparatus, and/or remote or “cloud”processors.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor. Alternatively, or additionally, the processor may beconfigured to execute hard-coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to an embodimentof the present invention while configured accordingly. Alternatively, asanother example, when the processor is embodied as an executor ofsoftware instructions, the instructions may specifically configure theprocessor to perform the algorithms and/or operations described hereinwhen the instructions are executed.

In some embodiments, the apparatus 200 may include input/outputcircuitry 206 that may, in turn, be in communication with processor 202to provide output to the user and, in some embodiments, to receive anindication of a user input. The input/output circuitry 206 may comprisea user interface and may include a display and may comprise a web userinterface, a mobile application, a client device, a kiosk, or the like.In some embodiments, the input/output circuitry 206 may also include akeyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, amicrophone, a speaker, or other input/output mechanisms. The processorand/or user interface circuitry comprising the processor may beconfigured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 204, and/or the like).

The communications circuitry 208 may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from/to anetwork and/or any other device, circuitry, or module in communicationwith the apparatus 200. In this regard, the communications circuitry 208may include, for example, a network interface for enablingcommunications with a wired or wireless communication network. Forexample, the communications circuitry 208 may include one or morenetwork interface cards, antennae, buses, switches, routers, modems, andsupporting hardware and/or software, or any other device suitable forenabling communications via a network. Additionally or alternatively,the communication interface may include the circuitry for interactingwith the antenna(s) to cause transmission of signals via the antenna(s)or to handle receipt of signals received via the antenna(s).

Cohort management circuitry 210 includes hardware configured to identifyand manage consumer cohorts. The cohort management circuitry 210 mayutilize processing circuitry, such as the processor 202, to performthese actions. The cohort management circuitry 210 may send and/orreceive data from binary promotion prediction circuitry 212 and/ornumber of promotion prediction circuitry 214. In some implementations,the sent and/or received data may be data identifying a plurality ofcohorts and data identifying how the cohorts are defined. In someimplementations, the data may include a normalized score for eachidentified score, the cutoff score indicating a cutoff score below whichconsumers belonging to a particular cohort are identified as unlikely tomake a purchase during a pre-specified period. For example, a normalizedscore may be determined for consumers belonging to first cohort bybinary prediction circuitry 212. Consumers having a normalized scoreabove the received cutoff score may be identified as consumers that arelikely to make, at least, one purchase during the pre-specified period.It should also be appreciated that, in some embodiments, the cohortmanagement circuitry 210 may include a separate processor, speciallyconfigured field programmable gate array (FPGA), or application specificinterface circuit (ASIC) to classify, update classifications and/ormanage classifications of consumers, and train and use a machinelearning model for predicting consumer behavior. In someimplementations, binary promotion prediction circuitry 212 and number ofpromotion prediction circuitry 214, described below, may besub-circuitry belonging to cohort management circuitry 210. The cohortmanagement circuitry 210 may be implemented using hardware components ofthe apparatus configured by either hardware or software for implementingthese planned functions.

Binary promotion prediction circuitry 212 includes hardware configuredto identify and determine a prediction specifying consumer behaviorduring a pre-specified period. In some implementations, the predictionis specified as a probability or likelihood that a consumer will make apurchase during a pre-specified period. In some implementations, theprediction is specified as an indication of whether a consumer will makea purchase during a pre-specified period. The binary promotionprediction circuitry 212 may utilize processing circuitry, such as theprocessor 202, to perform these actions. However, it should also beappreciated that, in some embodiments, the binary promotion predictioncircuitry 212 may include a separate processor, specially configuredfield programmable gate array (FPGA), or application specific interfacecircuit (ASIC) for determining a prediction specifying a likelihood thata promotion will be terminated. Similarly, number of promotionsprediction circuitry 214 includes hardware configured to identify anddetermine a prediction of a number of promotions a consumer will acceptduring a pre-specified period. Such prediction may also be specified asa probability or a likelihood. Again, it should also be appreciatedthat, in some embodiments, the number of promotions prediction circuitry214 may include a separate processor, specially configured fieldprogrammable gate array (FPGA), or application specific interfacecircuit (ASIC) for determining a number of promotions that are likely tobe accepted by a consumer during a pre-specified period. Circuitry 212and 214 may be implemented using hardware components of the apparatusconfigured by either hardware or software for implementing these plannedfunctions.

As will be appreciated, any such computer program instructions and/orother type of code may be loaded onto a computer, processor or otherprogrammable apparatus's circuitry to produce a machine, such that thecomputer, processor or other programmable circuitry that execute thecode on the machine create the means for implementing various functions,including those described herein.

It is also noted that all or some of the information presented by theexample displays discussed herein can be based on data that is received,generated and/or maintained by one or more components of apparatus 200.In some embodiments, one or more external systems (such as a remotecloud computing and/or data storage system) may also be leveraged toprovide at least some of the functionality discussed herein.

As described above and as will be appreciated based on this disclosure,embodiments of the present invention may be configured as methods,mobile devices, backend network devices, and the like. Accordingly,embodiments may comprise various means including entirely of hardware orany combination of software and hardware. Furthermore, embodiments maytake the form of a computer program product on at least onenon-transitory computer-readable storage medium having computer-readableprogram instructions (e.g., computer software) embodied in the storagemedium. Any suitable computer-readable storage medium may be utilizedincluding non-transitory hard disks, CD-ROMs, flash memory, opticalstorage devices, or magnetic storage devices.

Example Electronic Marketing Information Service Data Flow

FIG. 3 depicts an example data flow 300 illustrating interactionsbetween a server 302, one or more consumer devices 304, and one or moremerchant devices 306. The server 302 may be implemented in the same or asimilar fashion as the server 104 as described above with respect toFIG. 1, the one or more consumer devices 304 may be implemented in thesame or a similar fashion as the consumer devices 108A-108N as describedabove with respect to FIG. 1, and the one or more merchant devices 306may be implemented in the same or a similar fashion as the merchantdevices 110A-110N as described above with respect to FIG. 1.

The data flow 300 illustrates how electronic information may be passedamong various systems when employing a server 302 in accordance withembodiments of the present invention. The one or more consumer devices304 and/or one or more merchant devices 306 may provide a variety ofelectronic marketing information to the server 302 for use in providingpromotion and marketing services to the consumer. This electronicmarketing information may include, but is not limited to, location data,clickstream data, transaction data, communication channel data,historical data, review data and/or discretionary data.

As a result of transactions performed between the one or more consumerdevices 304 and the server 302, the server 302 may provide fulfillmentdata to the consumer devices. The fulfillment data may includeinformation indicating whether the transaction was successful, thelocation and time the product will be provided to the consumer,instruments for redeeming promotions purchased by the consumer, or thelike.

In addition to the e-commerce interactions with the one or more consumerdevices 304 offered by the server 302, the server 302 may leverageinformation provided by the consumer devices to improve the relevancy ofmarketing communications to individual consumers or groups of consumers.In this manner, the server 302 may determine promotions, goods, andservices that are more likely to be of interest to a particular consumeror group of consumers based on clickstream data, location data, andother information provided by and/or relating to particular consumers.For example, the server 302 may detect the location of a consumer basedon location data provided by the consumer device, and offer promotionsbased on the proximity of the consumer to the merchant associated withthose promotions.

Alternatively, the server 302 may note that the consumer has an interestin a particular hobby (e.g., skiing) based on electronic marketinginformation associated with the consumer (e.g., a browser cookie thatindicates they frequently visit websites that provide snowfall forecastsfor particular ski resorts), and offer promotions associated with thathobby (e.g., a promotion offering discounted ski equipment rentals orlift tickets). It should be appreciated that a variety of differenttypes of electronic marketing information could be provided to theserver 302 for the purpose of improving the relevancy of marketingcommunications. It should also be appreciated that this electronicmarketing information may be received from a variety of electronicsources, including various consumer devices, merchant devices, and othersources both internal and external to a promotion and marketing service.For example, other data sources may include imported contact databasesmaintained by merchants, electronic survey questions answered byconsumers, and/or various other forms of electronic data.

It should also be appreciated that the server 302 may also control otherfactors of the electronic marketing communications sent to the consumerother than the particular promotions included in the electronicmarketing communication. For example, the server 302 may determine theform, structure, frequency, and type of the electronic marketingcommunication. As with the content of the electronic marketingcommunication, these factors may be programmatically determinedaccording to various methods, factors, and processes based on electronicdata received by the server 302 for the purpose of maximizing thelikelihood that the communication will be relevant to the recipientconsumer.

The server 302 interactions with the one or more merchant devices 306may be related to enabling the merchant to market their products using apromotion and marketing service. For example, the one or more merchantdevices 306 may provide promotion data defining one or more promotionsto be offered by the promotion and marketing service on behalf of themerchant. The server 302 may receive this information and generateinformation for providing such promotions via an e-commerce interface,making the promotions available for purchase by consumers. The server302 may also receive information about products from the one or moremerchant devices 306. For example, a merchant may provide electronicmarketing information indicating particular products, product prices,inventory levels, and the like to be marketed via a promotion andmarketing service. The server 302 may receive this information andgenerate listing information to offer the indicating products toconsumers via a promotion and marketing service.

The one or more merchant devices 306 may also receive information fromthe server 302. For example, in some embodiments a merchant may obtainaccess to certain business analytic data aggregated, generated, ormaintained by the server 302. As a particular example, a merchant mightoffer to pay for consumer demographic data related to products orservices offered by the merchant. It should be appreciated, however,that a merchant may not need to list any products or services via thepromotion and marketing service in order to obtain such data. Forexample, the promotion and marketing service may enable merchants toaccess electronic marketing data offered via the promotion and marketingservice based on a subscription model. The one or more merchant devices306 may also receive electronic compensation data from the server 302.For example, when a promotion or product is sold by the promotion andmarketing service on behalf of the merchant, a portion of the receivedfunds may be transmitted to the merchant. The compensation data mayinclude information sufficient to notify the merchant that such fundsare being or have been transmitted. In some embodiments, thecompensation data may take the form of an electronic wire transferdirectly to a merchant account. In some other embodiments, thecompensation data may indicate that a promotion or product has beenpurchased, but the actual transfer of funds may occur at a later time.For example, in some embodiments, compensation data indicating the saleof a promotion may be provided immediately, but funds may not betransferred to the merchant until the promotion is redeemed by theconsumer.

Embodiments advantageously provide for improvements to the server byallowing prediction of consumer behavior more efficiently andaccurately. In turn, this reduces the server stress. For example, byeliminating provisioning of promotions that are unlikely to be acceptedby consumers, the server reduces the processing associated with offeringpromotions without reducing the overall number of promotions accepted byconsumers. In turn, the processing power requirement of the server isreduced and the overall revenue is increased.

Example Processes for Predicting Consumer Behavior

As described, a promotional and marketing service may utilize theinternet to provide consumers with available promotions related toproducts, services or experiences offered by providers that may be ofinterest to the consumers. However, determining which consumer toprovide promotions or advertisements to may prove challenging. Apromotion and marketing service may spend limited resources finding,engaging, and advertising to consumers. However, some consumers are lesslikely to make a purchase or accept a promotion in response to theefforts above. Other consumers may be very responsive to such efforts.

Naturally, the ability to accurately predict consumer behavior cansignificantly improve revenue and user satisfaction. As described, bothconsumers and promotional and marketing services can benefit greatlyfrom such accurate predictions. Additionally, predicting consumerbehavior can enable the promotional and marketing service to optimizeand adjust various financial, business, and marketing aspects based onthe predictions. Accordingly, there exists a dire market need formethods and systems that can accurately predict consumer behavioraccordingly.

FIG. 4 is a flow chart of an example process 400 for predicting consumerbehavior. The process 400 begins with receiving data for a firstconsumer of a plurality of consumers (402). In some implementations, theconsumer may be a consumer or a potential consumer of a promotional andmarketing service. The promotional and marketing service may be aservice for electronically providing promotions for a second entity(e.g., third party). For example, the second entity may be a departmentstore that is requesting transmittal of a promotion for goods (e.g.,clothing, shoes, toys, food items, camping equipment) offered by thedepartment store. In some implementations, the promotion may be for anyof the goods offered by the department store. In some implementations,the promotion may be for a specific type of goods offered by thedepartment store (e.g., camping equipment). In some implementations, thepromotion may be for a specific item, such as, a bike. Similarly, thesecond entity may be a service store, such as a hair salon or a carrepair shop. In such implementations, the service store may requesttransmittal of promotion for services provided by the store (e.g.,haircut, hair coloring, oil change). In some implementations, stores mayoffer a combination of services and goods.

In some implementations, the received data associated with the firstconsumer is historical data. For example, the received data may be dataindicating the number of promotions previously accepted by the consumer.The data may indicate the time and a device used to accept thesepromotions. Similarly, the data may include a measure of frequencyindicating how often promotions are accepted by the consumer. In someimplementations, the data may include location data indicating, forexample, locations at which the consumer accepted promotions. In someimplementations, the data may include data specifying types ofpromotions, goods, and/or services that are of interest to the consumer.For example, the data may include discretionary data for the consumer asdescribed above.

The process 400 continues with determining a classification for thefirst consumer, wherein the classification is based on a measure offrequency of purchases by the first consumer (404). For example, themeasure of frequency of purchases by the first consumer may be dataindicating a number of promotions accepted from the promotional andmarketing service during a pre-specified period (e.g., 1 month, 3 month,6 month, 1 year). In some implementations, the pre-specified period is aperiod directly before receiving the data at step 402. For example, themeasure of frequency may indicate the number of promotions accepted bythe first consumer during the last 3 months prior to receiving the dataat step 402.

In turn, the process 400 may classify the first consumer as belonging toa cohort or a group having a purchase frequency similar to the purchasefrequency of the first consumer. For example, consumers may beclassified as belonging to one of 6 exemplary cohorts. In oneimplementation, the pre-specified period may be 1 year. A first cohortmay be identified with “−1” as a cohort for consumers that neveractivated their respective account associated with the promotional andmarketing service. A second cohort may be identified with “0” as cohortfor consumers that activated their respective account but do not havesufficient data for the pre-specified period. For example, suchconsumers may have recently activated their respective account.Accordingly, these consumers may not have activated their account for atleast a year. For example, these consumers may have activated theirrespective account during the previous 6 months. Accordingly, there isinsufficient information for these consumers.

The third cohort “one-time buyers” may be identified with the number“1.” Consumers that made a single purchase during the past year areclassified as consumers belonging to the “one-time buyer” cohort.Similar, a fourth cohort for “sporadic buyers” may be identified withthe number “2.” Consumers that made, for example, 2 purchases during theprevious year may be classified as belonging to this cohort. A fifthcohort for “loyal buyers” may be identified by the number “3.” Consumersthat made, for example, 3-7 purchases during the previous year may beclassified as belonging to this cohort. Finally, a cohort for “powerusers” is identified with the number “4”. For example, consumers thatmade 8 or more purchases during the previous year may be classified asbelonging to the “power users” cohort. In some implementations, thenumber of cohorts may be different. Similarly, in some implementations,the pre-specified period and the range of purchases for each cohort maybe different. FIGS. 8A and 8B depict some statistical data associatedwith the exemplary cohorts described above.

The process 400 may continue with identifying one or more firstattributes for the first consumer based on the determinedclassification, the one or more attributes being attributes selected forpredicting the respective one or more metric associated with the firstconsumer (406). For example, a first set of attributes may be identifiedfor the first consumer based on the cohort to which the first consumerbelongs. For example, if the first consumer is identified as belongingto the “one time buyer” cohort, a set of attributes associated with the“one-time buyer” cohort is identified for the first consumer. Similarly,if the first consumer is identified as belonging to the “power buyer”cohort, a set of attributes associated with the “power buyer” cohort isidentified for the first consumer. Exemplary attributes for theexemplary cohorts are discussed in more detail below, with reference toFIG. 11A-11D.

In some implementations, the process 400 may continue with optional step410 shown in phantom. The process 400 may continue with determining,based on values of third attributes different from the first attributes,a third prediction specifying whether the first consumer will make apurchase within a pre-specified time period (410). For example, a set ofthird attributes may be identified based on the first consumerclassification. The third attributes may be for predicting whether thefirst consumer will make a purchase or accept a promotion within thepre-specified period. In some implementation, this prediction is abinary “yes” or “no” prediction. In some implementations, the process400 may determine a score indicating the likelihood that the firstconsumer is going to make a purchase during the pre-specified period. Acutoff score may be defined for each cohort, such that if a score of aconsumer belonging to that cohort exceeds the cutoff score, the process400 predicts that the consumer will make a purchase. Similarly, if thescore for the first consumer is below the cutoff score, the process 400may predict that the consumer is not going to make a purchase.

In some implementations, the score is a normalized score between thevalues of 0 and 1. As described, the score may represent a probabilityor likelihood that a consumer will make a purchase or accept apromotion. A cutoff score or percentage may be specified by thepromotional and marketing service performing process 400. For example, acutoff score of 0.7 or higher may result in a prediction indicating thatthe consumer will make a purchase. Naturally, a cutoff percentage orscore may be customized in order to maximize accuracy of predictions.Exemplary cutoff scores and accuracy metrics for cohorts are shown inFIGS. 8A and 8B.

In some implementations, the third attributes for a consumer classifiedas “power user” may be different from the third attributes for aconsumer classified as “one-time buyer.” Similarly, the third attributesfor a consumer classified as “power user” may be different from thefirst attributes for the same consumer. Exemplary attributes for theexemplary cohorts are discussed in more detail below, with reference toFIGS. 11A-11D.

Responsive to predicting that the fist consumer is not going to make apurchase, the process 400 may end (411). Similarly, responsive topredicting that the fist consumer is going to make a purchase, theprocess 400 continue to step 412. Alternatively, since steps 410 and 411are optional, the process 400 may continue directly from step 406 to412.

Finally, the process 400 may continue with determining, based on valuesfor the one or more first attributes, a first prediction value thatindicates a programmatically expected number of purchases by the firstconsumer (412). For example, the process 400 may determine based on theidentified first attributes that the first consumer is likely topurchases 5 items during the next or upcoming year. In someimplementations, the pre-specified period may be a year. In someimplementations, the pre-specified period may be a month, 3 months, 6months or the like.

In some implementations, the process 400 may continue with optionalsteps of process 500, shown in phantom in FIG. 5. The process 500 mayoptionally apply a decay factor to the first prediction value, the firstdecay factor being a factor that proportionally reduces the firstprediction value, wherein the first decay factor is determined based ona measure of time lapsed between a time of the determination of thefirst prediction value and a time when the decay factor is applied(502). For example, if 3 months elapsed after determining the firstprediction, a decay factor (e.g., a value between 0 and 1) is applied to(e.g., multiplied by) the first prediction. For example, a decay factormay be applied to a first prediction indicating that that a particular“power user” is going to accept 10 promotions during the next year. Thedecay factor corresponding to the 3 month period may be “0.8”.Accordingly, the adjusted first prediction may be 8 (0.8×10). In someimplementations, the decay factor may similarly be applied to the binaryscore for determining whether a consumer will make a purchase during thepre-specified time.

In some implementations, the process 500 may optionally continue withreceiving updated first values for the one or more first attributes(504). For example, the process 500 may receive a “n_emails_send”attribute. The “n_emails_send” may indicate the number of emails sent toa consumer, for example, during the past 3 months. Such attribute may befrequently changing, as new emails are being sent to the consumer.

In some implementations, the process 500 may optionally continue withdetermining that a first event associated with the first consumeroccurred (506). For example, the process 500 may determine that theconsumer made a new purchase or accepted a new promotion. In response todetermining that the first event occurred, the process 500 may determinebased on the updated first values for the one or more first attributes,an updated prediction value that indicates an updated programmaticallyexpected number of purchases by the first consumer (508). For example,the process 500 may recalculate and update the programmatically expectednumber of purchases by the first consumer, in response to determine thatthe first consumer made a new purchase or accepted a new promotion. Insome implementations, updates that require recalculation ofprogrammatically expected numbers of purchases may only be performed inresponse to detecting a particular trigger event. In someimplementations, the trigger event is making a new purchase or acceptinga new promotion. In some implementations, the trigger event may be, forexample, making a threshold number of new purchases, detectingparticular changes within historical data associated with the respectiveconsumer, reaching a particular calendar date, detecting changes withinhistorical data of a group of consumers or a cohort, or the like.Accordingly, computer or processor intensive prediction recalculationsmay be performed in response to events that are likely to substantiallychange the prediction.

In some implementations, the process 400 or the process 500 may continuewith the following optional steps of process 600, shown in phantom inFIG. 6. The process 600 may optionally receive data for a secondconsumer from the plurality of consumers, the second consumer beingdifferent from the first consumer. For example, the second consumer maybe a consumer or a potential consumer of a promotional and marketingservice different from the first consumer. In some implementations, thereceived data for the second consumer is historical data. For example,the received data may be data indicating the number of promotionspreviously accepted by the second consumer. The data may indicate thetime and a device used to accept these promotions. Similarly, the datamay include a measure of frequency indicating how often promotions areaccepted by the consumer. In some implementations, the data may includelocation data indicating, for example, locations at which the consumeraccepted promotions. In some implementations, the data may include dataspecifying types of promotions, goods, and/or services that are ofinterest to the consumer. For example, the data may includediscretionary data for the second consumer as described above.

In some implementations, the process 600 optionally continues withdetermining, a classification for the second consumer, wherein theclassification is based on a measure of frequency of purchases by thesecond consumer, and wherein the classification of the second consumeris different from the classification of the second consumer (604). Forexample, the measure of frequency of purchases by the second consumermay be data indicating a number of promotions accepted from thepromotional and marketing service during a pre-specified period (e.g., 1month, 3 months, 6 months, 1 year). In some implementations, thepre-specified period is a period directly before receiving the data atstep 604. For example, the measure of frequency may indicate the numberof promotions accepted by the second consumer during the last 3 monthsprior to receiving the data at step 402.

In turn, the process 400 may classify the second consumer as belongingto a cohort or a group having a purchase frequency similar to thepurchase frequency of the second consumer. For example, consumers may beclassified as belonging to one of the 6 exemplary cohorts describedabove with reference to the second consumer.

The process 600 may optionally continue with identifying, one or moresecond attributes for the second consumer based on the determinedclassification for the second consumer, the one or more secondattributes being attributes selected for predicting the respective oneor more metric associated with the second consumer, and wherein theclassification of the second consumer is different from theclassification of the first consumer (610). For example, a second set ofattributes may be identified for the second consumer based on the cohortto which the second consumer belongs. For example, if the secondconsumer is identified as belonging to the “sporadic buyers” cohort, aset of attributes associated with the “sporadic buyers” cohort isidentified for the second consumer. Similarly, if the second consumer isidentified as belonging to the “loyal buyers” cohort, a set ofattributes associated with the “loyal buyers” cohort is identified forthe second consumer.

The process 600 may optionally continue with determining, based onvalues for the one or more second attributes, a second prediction valuethat indicates a programmatically expected number of purchases by thesecond consumer, wherein the second prediction value is different fromthe first prediction value (612). For example, the process may 600 maydetermine based on the identified second attributes that the secondconsumer is likely to purchase 2 items during the next or upcoming year.In some implementations, the pre-specified period may be a year. In someimplementations, the pre-specified period may be a month, 3 months, 6months or the like.

In some implementations, the processes 400, 500, or 600 may continuewith the following optional steps of process 700, shown in phantom inFIG. 7. The process 700 may optionally determine a prediction specifyingan expected number of sales for a seller during a pre-specified periodof time based, at least in part, on the first prediction and the secondprediction (702). For example, the process 700 may determine an expectednumber of sales by the promotional and marketing service based on aplurality of predictions for a plurality of consumers including thefirst and the second consumers. In some implementations, the process 700may add or compile predictions for all consumers and potentialconsumers. In some implementations, the process 700 may add or compilepredictions for a group or a cohort of consumers and/or potentialconsumers.

In some implementations, the process 700 may optionally continue withproviding an advertisement to the first consumer and not providing anadvertisement to the second consumer, based, at least in part, on thefirst prediction and the second prediction (704). For example, with thereference to the examples above, predictions specify that the firstconsumer is likely to purchase or accept more promotions than the secondconsumer. Accordingly, the first consumer may be targeted foradvertisements. For example, the process 700 may provide anadvertisement to the first consumer and not provide an advertisement tothe second consumer. Alternatively, advertisement efforts and campaignmay target consumers less likely to make a purchase, in order toincrease the likelihood of these consumers making a purchase. Forexample, the process 700 may provide an advertisement to the secondconsumer and not provide an advertisement to the second consumer.

In other implementations, the process 700 may alternatively andoptionally continue with providing advertisements to the first consumerat a different rate from providing advertisements to the secondconsumer, based, at least in part, on the first prediction and thesecond prediction. For example, with the reference to the examplesabove, predictions specify that the first consumer is likely to purchaseor accept more promotions than the second consumer. Accordingly, thefirst consumer may be targeted for advertisements more than the secondconsumer. For example, the process 700 may provide advertisements to thefirst consumer at a higher rate than providing advertisements to thesecond consumer. Alternatively, advertisement efforts and campaign maytarget consumers less likely to make a purchase, in order to increasethe likelihood of these consumers making a purchase. For example, theprocess 700 may provide advertisements to the second consumer at ahigher rate than the first consumer.

In some implementations, predictions may be used to analyze events thatmay affect consumer value. For example, by analyzing interactions, by aparticular consumer, with customer service, a return on investment (ROI)may be determined for the particular consumer. Further, a customerlifetime value may be a metric that can be optimized for productexperimentation.

FIG. 8A depicts a graphical representation of the cutoff score againstthe percentage of correct binary predictions. Elements 802, 804, 806,808 and 810 correspond to cohorts “0,” “1,” “2,” “3,” and “4”respectively. FIG. 8B shows exemplary statistical data, during a testperiod, for different cohorts. As can be seen in FIG. 8B the accuracy ofpredictions may vary for different cohorts. The variation may be basedon the selected attributes for training a respective model for arespective cohort. Also, the variation may be based on a selected cutoffscore for each respective cohort. Other metrics that the promotional andservice data may wish to optimize are also shown. For example, thepromotional and marketing service may wish to minimize a recallpercentage for some cohorts. Additionally, the promotional and marketingservice may wish to avoid marketing campaigns targeting cohorts having ahigh recall percentage. This statistical data may be used to determineoptimized cutoff scores or percentages, as described above.Additionally, this statistical data may be used to determine the mostimportant attributes as will be discussed in the following section inmore detail.

Example Consumer Behavior Prediction Attributes and Selection Processes

FIGS. 11A-11D shows lists of example attributes, for predicting consumerbehaviors, and an associated ranking indicating a measure of importanceof each attribute for a plurality of cohorts. A list of exemplaryattributes is identified for each stage of a 2 stage prediction process.Stage 1 may be for predicting whether a consumer will make a purchaseduring a pre-specified period. Stage 2 may be for predicting aprogrammatically expected number of purchases a consumer will makeduring the pre-specified period. In some implementations, a predictionaccording to stage 2 is only performed in response to a stage 1prediction indicating that the consumer will make a purchase during thepre-specified period.

In one example, the attribute “goods_cohort” specifies a year aparticular consumer accepted or purchased his/her first promotion. Insome implementations, the “goods_cohort” attribute is highly ranked forparticular combinations of cohorts and stages. Therefore, the“goods_cohort” is an important and effective attribute for predictionsassociated with the particular combination. Similarly, the attribute“days_since_order” indicating a number of days lapsed since a particularconsumer made their last order, accepted their last promotion, or madetheir last purchase, is highly ranked for particular combinations ofcohorts and stages. Accordingly the “days_since_order” attribute is animportant and effective attribute for consumer behavior predictions. Anattribute “gb_1yr” indicating a number of gross bookings of promotionsmay be highly ranked for particular combinations of cohorts and stageswhile being not highly ranked for other combinations. Therefore, the“gb_1yr” attribute may be used only for some combinations.Alternatively, a lower emphasis may be associated with the “gb_1yr”attribute for some combinations. For example, weights may be associatedwith attributes. Higher weights may be assigned to higher rankingattributes. Similarly, lower weights may be assigned to lower rankingattributes.

In some implementations, lower ranking attributes may be effectivesecondary attributes that complement important attributes for consumerbehavior predictions. Similarly, highly ranked attributes may beeffective as secondary attributes that complement important attributesfor consumer behavior predictions. As described above, secondaryattributes may be assigned lower weights than highly ranked attributesor primary attributes. In some implementations, the top ranking 5attributes are identified as primary attributes. It should be understoodthat each of the attributes discussed in this application, may be insome embodiments, a primary or a secondary attribute. A list ofexemplary attributes is provided below.

In some implementations, multiple attributes may represent historicaldata for a particular consumer. For example, some attributes may beassociated with discretionary data for the consumer. Other attributesmay be associated with the number of promotions accepted by a consumer.Some attributes may indicate a measure of frequency at which a consumeraccept promotions or makes purchases. Some attributes may indicate anumber of promotions accepted during a plurality of different periods.Some attributes may indicate a number or a percentage of unredeemedpromotions accepted or purchased by the consumer. Some attributes mayflag the consumer as belonging to a particular group or cohort. Todetermine a normalized value of such attributes, an algorithm may beexecuted on associated raw data related to a consumer. Accordingly,normalized scores, numbers, or flags can be determined for eachattribute.

As can be seen in FIGS. 11A-11D, a number of example attributes relateto platform data. For example, the platform data may indicate a type ofa device used to accept a promotion or make a purchase. In someimplementations, the platform data may indicate a primary or a last typeof a device used to accept a promotion or make a purchase. Similarly,platform data may indicate the number of different platforms (e.g.,mobile, email, web) used to accept promotions or make purchases by aconsumer. There is also a number of attributes related to location data.It should be understood that attribute data related to location andhyper location data may be generated in a similar fashion. For example,“n_deals” attribute may specify a number of deals available in a 5-mileradius from the location of the respective consumer during apre-specified period. For example, “n_deals” attribute may specify anumber of deals available in a 5-mile radius from the location of therespective consumer during the last quarter. Similarly, the attribute“n_hero_deals” may specify a number of hero deals available in a 5-mileradius from the consumer during the last quarter. A hero deal may be,for example, a deal that accepted by more than a threshold number ofconsumers. For example, a hero deal may be a deal that accepted by morethan 1000 consumers. In should be understood that the radius of thelocation above may be adjusted to optimize various aspects of theinvention. Other location attributes may specify a distance from theclosest city center and a size of the closest city. For example, theclosest city may be identified as one of a list of predefined sizes forcities.

In some implementations, one or more attributes may be associated withemails and email traffic from and to the promotional and marketingservice. For example, the attribute “es_email” may correspond to anemail engagement score (ES). In some implementations, the emailengagement score is based, at least in part, on an opening rate foremails and a click or interaction rate with emails. For example, anopening rate for emails may be a ratio of emails, from the promotionaland marketing service, opened by a consumer against emails sent by thepromotional and marketing service to the consumer. Similarly, aninteraction or click rate for emails may be a ratio of emails, from thepromotional and marketing service, interacted with by the consumeragainst emails, from the promotional and marketing service, opened bythe consumer. In some implementations, interaction with an email may beclicking a redirection link associated with the email. For example, theconsumer may click a link that redirects the consumer to web page of thepromotional and marketing service for purchasing a promotion associatedwith the email. In some implementations, the interaction may be hoveringa pointing device over a link or an advertisement in the email. In someimplementations, viewing an email for a period of time exceeding apre-specified threshold may be considered an interaction.

In some implementations, a ‘pct_wow’ attribute specifies a percentage ofpromotions that are classified as “WOW” deals. In some implementations,“WOW” deals may be deals offered by merchants having a pre-specifiednational presence. For example, “WOW” deals may be deals offered bymerchants that offer deals in at least 5 different countries. Similarly,WOW” deals may be deals offered by merchants that offer deals in atleast 21 different countries. A “goods_upward_trend” attribute mayindicate whether a percentage of purchases for a type of goods isincreasing year over year (Y-O-Y). For example, an indication may beassociated with electronic merchandise specifying that purchases by theelectronic merchandise is increasing yearly.

Other exemplary attributes may include attributes corresponding to usersatisfaction. In one implementation, such attributes may include anattribute specifying a number of calls made to customer service by therespective consumer during a pre-specified period. For example, theattribute may specify a number of customer service calls initiated bythe consumer during the last year. Similarly, an overall wait timeduring customer service calls may be one attribute. In someimplementations, the attributes may include attributes associated withpositive and negative responses to surveys. For example, one attributemay specify a number of negative responses to surveys by the consumer.Similarly, one attribute may specify a number of positive responses tosurveys by the consumer. Some attributes may be associated with datafrom review websites such as Yelp®, Google®, Yahoo®, City Search®, TripAdvisor®. It should be understood that any review website could haveassociated attribute data, such as for example Zagat®, Bing® or thelike. It should also be understood that attribute data may be associatedwith reviews from a particular consumer. Attribute data may be generatedrelated to positive and negative reviews provided by respectiveconsumers.

It should also be understood that consumers may have associated dataindicating one or more categories, sub-categories, location,hyper-locations, prices or the like. For example, a consumer, may beidentified as a consumer that is interested in categories such as“beauty, wellness, and healthcare,” “Food and drink,” “Leisure Offersand Activities.” Similarly, a consumer may be identified as a consumerinterested in promotions within a pre-specified price range. Forexample, a consumer may be identified as a consumer that is likelyinterested in promotions costing less than $20. Attributes correspondingto such data may also be used for predicting consumer behavior.

Additionally or alternatively, some implementations may include at leastone of “years in file” indicating the number of years a consumer hasbeen consumer. As described above, consumers may be divided into cohortscorresponding to (1) a time measure indicating how long a consumer was acustomer of the promotional and marketing service, (2) the number ofpurchases made by the consumer during a first pre-specified period,and/or (3) the expected number of purchases to be made by the consumerduring a second pre-specified period. Each cohort may utilize differentattributes for predicting consumer behavior.

Similar attributes may be clustered, grouped, or aggregated. Forexample, attributes associated with locations or location basedattributes may be grouped under header attribute “location.” Forexample, a division attributes specifying a size of the division wherethe consumer resides and an attribute specifying a distance from acenter of a city where a promotion is offered may be clustered under thelocation header attribute. Similarly, attributes associated with“historical data,” “category & services of interest,” “discretionarydata,” and/or “review/survey data” may each also be clustered and/orgrouped under header attributes. In some implementations, one or moreattributes under the same header may be combined. In otherimplementations, more granular attributes under a particular header maybe added.

In some implementations, the header attributes are ranked according tothe overall importance and effectiveness of the attributes belonging tothe header attribute. In other implementations, each attribute is rankedindependently, for each stage and for each cohort, as shown in FIGS.11A-11D.

Below is a list of exemplary attributes for consumer behaviorprediction:

-   -   ‘gb_life’: Gross Bookings (GB) over entire lifetime    -   ‘gb_1yr’: GB inpast year    -   ‘gb_qtr’: GB in past quarter    -   ‘sub_cohort’: subscription year    -   ‘act_cohort’: activation (first purchase) year    -   ‘act_channel’: activation channel (Local, Goods, or Other)    -   ‘days_to_order’: days between subscription and first order    -   ‘goods_cohort’: year of first goods purchase    -   ‘app_cohort’: year of first app download    -   ‘mobile_device’: type of mobile device (e.g. iPhone)    -   ‘n_local_subs’: # of Local email subscriptions    -   ‘discretionary_data’: discretionary consumer data    -   ‘subdiv_peer_group’: subdivision peer group (e.g. city center vs        suburbs)    -   ‘city_dist’: distance from city center    -   ‘div_size’: division size (tiny, small, medium, large, huge)    -   ‘n_deals’: # deals in a 5-mile radius in past quarter    -   ‘n_hero_deals’: # hero (high GB) deals in a 5-mile radius in        past quarter    -   ‘n_cstix_1yr’: # customer service tickets in the past year    -   ‘n_good’: # good CS survey responses in past year    -   ‘n_bad’: # bad CS survey responses in past year    -   ‘email_wait_minutes’: max CS email wait in minutes in past        quarter    -   ‘phone_wait_seconds’: max CS phone wait in seconds in past        quarter    -   ‘n_refunds_1yr’: # refunds in past year    -   ‘es_email’: email engagement score in past quarter    -   ‘n_emails_send’: number of emails sent in past quarter    -   ‘es_app’: app engagement score in past quarter    -   ‘avg_ship_time’: average Goods shipping time    -   ‘n_platforms’: # order platforms in the past year    -   ‘primary_order_platform’: primary order platform in the past        year    -   ‘pct_non_local’: % orders non-Local in the past year    -   ‘goods_upward_trend’: y-o-y Goods % upward trend flag (0/1)    -   ‘n_unredeemed’: # unredeemed promotions    -   ‘gb_unredeemed’=GB in unredeemed promotions    -   ‘n_expired’: # expired promotions    -   ‘pct_redeemed’: % promotions redeemed    -   ‘avg_days_between’: avg # days between purchase and redemption    -   ‘pct_wow’: % promotions WOW deals (e.g. Starbucks)    -   ‘pct_incentive’: % promotions from incentives    -   ‘unsub’: 0/1 unsub_all status (1=unsubscribed from all emails)    -   ‘ords_per_sub’: orders per subscriber in home division    -   ‘days_since_order’: days since most recent order

These attributes and others may be computed periodically (e.g., daily,weekly, and monthly) for consumers. The clustered or the non-clusteredattributes may be used to train a machine learning model. It should beunderstood that the selection of attributes or clusters of attributesfor training machine learning models or for consumer behavior predictionprocesses can greatly affect the respective performance. In someimplementations, attributes and/or clusters of attributes are selectedbased on statistical analysis. In some implementations, selection of themost significant attributes is based on one or more different attributeselection approaches. These approaches may be (1) forward selection,which is starting with the most significant attributes and incrementallyadding a next significant attribute until the model is stable; (2)backward elimination, which starts with all the attributes and excludethe non-significant attributes one by one until the model is stable; (3)a combination of forward selection and backward elimination; and (4)checking the significance of the attribute by statistical model(regression). In one embodiment, each attribute selection approach maygive a subset of significant attributes. The attributes that are notshown to be significant by one or more of the attribute selectionapproaches may be excluded from the model.

In some implementations, the consumer behavior prediction process isperformed according to a random forest model. The model may operate byconstructing multiple decision trees at training. Each decision tree maybe based on different attributes. In some implementations, the randomforest model output is the mode of classes or the most occurring classamong all the trees of the random forest. In some implementations, therandom forest model is trained with historical data associated withvarious attributes. In some implementations, different trained modelsmay be utilized for different cohorts and/or stages. For example, eachof stage 1 and stage 2 for each of cohorts 1-4 may be trained accordingto a different model. For example, each combination of stages andcohorts may be trained according to the attributes shown in FIGS.11A-11D.

In some implementations, the number of cohorts is determined based onmarket analysis to provide a balance between model accuracy and systemstress and/or feasibility. For example, models having a large number ofcohorts may be very accurate. However, such models may take asignificantly longer amount of time to train. Alternatively, increasingthe number of cohorts over a particular threshold may result in nosignificant improvement in accuracy while significantly increasing therequired training time. The top attributes for each cohort may bedetermined according to random forest machine learning algorithm. Thetop attributes may be updated frequently. For example, the top attributemay change frequently for a particular cohort. Accordingly, the topattributes for that particular cohorts may be updated frequently. Insome implementations, the top attributes for all cohorts are updatedevery 3 month. In other implementations, the top attributes are updatedmonthly or yearly. In some implementations, testing may be performed todetermine the effectiveness of attributes prior to updating the topattributes.

FIGS. 9A and 9B are flow charts of an example processes 900 a and 900 bfor training a consumer behavior model based on selected attributes. Theprocess 900 a begins with selecting first attributes from an attributepool to generate a first decision tree, the first decision tree being amodel for whether the first consumer will make a purchase within apre-specified time period (902). For example, the process 900 a mayselect all “location” attributes and the “goods_cohort” attribute and/orother attributes or clusters of attributes. The selected firstattributes are used to generate a decision tree for predicting whetherthe first consumer will make a purchase within a pre-specified timeperiod. In some implementation, the selection is based on a machinelearning algorithm. In some implementations, the attributes are selectedbased on statistical analysis of past performances. In someimplementations, the pool of attributes comprises one or more of thelist of exemplary attributes above. In some implementations, theselected attributes are different for different combinations of cohortsand stages.

The process 900 a continues with selecting second attributes from theattribute pool to generate a second decision tree, the second attributesbeing different from the first attributes and the second decision treebeing a model for whether the first consumer will make a purchase withina pre-specified time period (904). The second attributes are selected ina manner similar to step 902. In some implementations, the secondattributes are different from the first attributes. The selected secondattributes are then used to generate a second decision tree. In someimplementations, the process 900 a continues with optional step 906shown in phantom. The process 900 a may select third attributes from anattribute pool to generate a third decision tree, the third decisiontree being a model for whether the first consumer will make a purchasewithin a pre-specified time period (906). In some implementations, thethird attributes are different from the first and second attributes. Insome implementations, the third attributes are then used to generate adecision tree. In some implementations, each generated tree is unique.In some implementations, additional trees are generated based on otherselections of attributes. In some implementations, the process 900 a maygenerate trees based on unique attribute selections until a thresholdnumber of trees are generated. In some implementations, the process 900a may generate trees based on unique attribute selections until theprediction model is stable and accurate.

The process 900 b is generally similar to 900 a. However, process 900 ais directed toward stage 1 and process 900 b is directed towards stage2. The process 900 b begins with selecting first attributes from anattribute pool to generate a first decision tree, the first decisiontree being a model for predicting a programmatically expected number ofpurchases (902 b). For example, the process 900 b may select the“goods_cohort” attribute and the “days_since_order” attribute and/orother attributes or clusters of attributes. The selected firstattributes are used to generate a decision tree for predicting aprogrammatically expected number of purchases during a pre-specifiedperiod. In some implementation, the selection is based on a machinelearning algorithm. In some implementations, the attributes are selectedbased on statistical analysis of past performances. In someimplementations, the pool of attributes comprises one or more of thelist of exemplary attributes above. In some implementations, theselected attributes are different for different combinations of cohortsand stages. In some implementations, the first attributes selected forprocess 900 a (stage 1) is different from the first attributes selectedfor process 900 b (stage 2). For example, the attributes shown in FIGS.11A-11D may be selected for each respective stage and cohortcombination.

The process 900 b continues with selecting second attributes from theattribute pool to generate a second decision tree, the second attributesbeing different from the first attributes and the second decision treebeing a model for predicting a programmatically expected number ofpurchases (904 b). The second attributes are selected in a mannersimilar to step 902 b. In some implementations, the second attributesare different from the first attributes. In some implementations thesecond attributes are different from the second attributes of process900 a. The selected second attributes are then used to generate a seconddecision tree. In some implementations, the process 900 b continues withoptional step 906 b shown in phantom. The process 900 b may select thirdattributes from an attribute pool to generate a third decision tree, thethird decision tree being a model for predicting a programmaticallyexpected number of purchases (906 b). In some implementations, the thirdattributes are different from the first and second attributes and fromall attributes of process 900 a. In some implementations, the thirdattributes are then used to generate a decision tree. In someimplementations, each generated tree is unique. In some implementations,additional trees are generated based on other selections of attributes.In some implementations, the process 900 b may generate trees based onunique attribute selections until a threshold number of trees aregenerated. In some implementations, the process 900 b may generate treesbased on unique attribute selections until the prediction model isstable and accurate.

FIGS. 10A and 10B are flow charts of example process 1000 a and 1000 bfor predicting consumer behavior, according to a model trained based onselected attributes. The process 1000 a begins with determining, for afirst consumer, a first prediction based on the first decision tree andthe first attributes, the first prediction being a prediction indicatingwhether the first consumer will make a purchase within a pre-specifiedtime period (1002). For example, the process 1000 a may determine afirst prediction specifying whether the first consumer will make apurchase within a pre-specified time period based on the “location”attributes and the “goods_cohort” attribute and/or other attributes orclusters of attributes. For example, the first prediction may indicatethat a particular consumer will make a purchase during an upcoming 6month period.

The process 1000 a continues with determining, for a first consumer, asecond prediction based on the second decision tree and the secondattributes, the second prediction being a prediction indicating whetherthe first consumer will make a purchase within a pre-specified timeperiod (1004). For example, the second prediction may also predict thatthe particular consumer will make a purchase during an upcoming 6 monthperiod. In some implementations, the process 1000 a may continue withdetermining, an overall prediction based at least in part on the first,and second predictions (1008). For example, since both the first andsecond predictions indicate that the particular consumer will make apurchase during the upcoming 6 months, the overall prediction may bethat the consumer will make a purchase during the upcoming 6 months,because the mode of the first and second predictions is such.

In some implementations, the process 1000 a includes optional step 1006shown in phantom. The process 1000 may determine, for a first consumer,a third prediction based on the third decision tree and the thirdattributes, the third prediction being a prediction indicating whetherthe first consumer will make a purchase within a pre-specified timeperiod (1008). In such implementations, the process 1000 includes thethird prediction in determining the mode of the prediction trees. Insome implementations, the additional predictions of additional trees mayalso be used to calculate the mode. In turn, the prediction for thefirst consumer is determined according to the mode. For example, out offive total predictions, if the first, third and fourth predictionsindicated that the consumer will not make a purchase during the upcoming6 month period, while the second and fifth predictions indicated thatthe first consumer will make a purchase during that 6 month period, thenthe overall prediction would be that first consumer will not make apurchase during the 6 month period.

The process 1000 b is generally similar to the process 1000. The process1000 b begins with determining, for a first consumer, a first predictionbased on the first decision tree and the first attributes, the firstprediction being a prediction indicating a programmatically expectednumber of purchases (1002 b). For example, the process 1000 b maydetermine a first prediction specifying programmatically expected numberof purchases within a pre-specified time period based on the“days_since_order” attribute and the “goods_cohort” attribute and/orother attributes or clusters of attributes. For example, the firstprediction may indicate that a particular consumer will make 10purchases during an upcoming 6 month period.

The process 1000 b continues with determining for a first consumer, asecond prediction based on the second decision tree and the secondattributes, the second prediction being a prediction indicating aprogrammatically expected number of purchases (1004 b). For example, thesecond prediction may predict that the particular consumer will make 2purchases during an upcoming 6 month period. In some implementations,the process 1000 b may continue with determining, an overall predictionbased at least in part on the first, and second predictions (1008 b).For example, since the first prediction indicated 10 purchases and thesecond predictions indicated 2 purchases for the particular consumerduring the upcoming 6 months, the overall prediction may be 6 purchasesduring the upcoming 6 months, because the mode of the first and secondpredictions is such.

In some implementations, the process 1000 b includes optional step 1006b shown in phantom. The process 1000 b may determine, for a firstconsumer, a third prediction based on the third decision tree and thethird attributes, the third prediction being a prediction indicating aprogrammatically expected number of purchases (1008 b). In suchimplementations, the process 1000 b includes the third prediction indetermining the mode of the prediction trees. In some implementations,the additional predictions of additional trees may also be used tocalculate the mode. In turn, the prediction for the first consumer isdetermined according to the mode.

Additional Implementation Details

Although an example processing system has been described in FIG. 2,implementations of the subject matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described hereincan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter describedherein can be implemented as one or more computer programs, i.e., one ormore modules of computer program instructions, encoded on computerstorage medium for execution by, or to control the operation of,information/data processing apparatus. Alternatively, or in addition,the program instructions can be encoded on an artificially-generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, which is generated to encode information/datafor transmission to suitable receiver apparatus for execution by aninformation/data processing apparatus. A computer storage medium can be,or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described herein can be implemented as operationsperformed by an information/data processing apparatus oninformation/data stored on one or more computer-readable storage devicesor received from other sources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor information/data (e.g., one or more scripts stored in a markuplanguage document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described herein can be performed by oneor more programmable processors executing one or more computer programsto perform actions by operating on input information/data and generatingoutput. Processors suitable for the execution of a computer programinclude, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions andinformation/data from a read-only memory or a random access memory orboth. The essential elements of a computer are a processor forperforming actions in accordance with instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive information/datafrom or transfer information/data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. However, a computer need not have such devices. Devicessuitable for storing computer program instructions and information/datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described herein can be implemented on a computer having adisplay device, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information/data to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described herein can be implemented ina computing system that includes a back-end component, e.g., as aninformation/data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described herein, or any combination of one or more suchback-end, middleware, or front-end components. The components of thesystem can be interconnected by any form or medium of digitalinformation/data communication, e.g., a communication network. Examplesof communication networks include a local area network (“LAN”) and awide area network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits information/data (e.g., an HTML page) toa client device (e.g., for purposes of displaying information/data toand receiving user input from a user interacting with the clientdevice). Information/data generated at the client device (e.g., a resultof the user interaction) can be received from the client device at theserver.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described herein in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation

The invention claimed is:
 1. A system, comprising a data processingapparatus and a computer memory apparatus in data communication with thedata processing apparatus and storing instructions executable by thedata processing apparatus and that upon such execution cause the dataprocessing apparatus to: receive data associated with a first consumerof a plurality of consumers; determine a first classification for thefirst consumer, wherein the first classification represents a firstcohort of a plurality of cohorts to which the first consumer belongs andis based at least in part on a measure of frequency of historicalpurchases by the first consumer; identify one or more first attributesof a plurality of attributes based on attributes associated with thefirst cohort, the one or more first attributes being attributes selectedfor predicting respective one or more metrics associated with the firstconsumer, wherein a set of attributes for each cohort of the pluralityof cohorts identified as having highest measures of importance forpredicting a given metric relative to other attributes for each cohortis determined according to a first trained machine learning modelcomprising a random forest model, wherein each decision tree of therandom forest model comprises unique attributes of the plurality ofattributes relative to other decision trees of the random forest model,and wherein the given metric is a prediction as to whether the firstconsumer will make a purchase within a pre-specified time period; selecta second trained machine learning model based on the first cohort, thesecond machine learning model comprising a plurality of decision treesgenerated based on the one or more first attributes associated with thefirst cohort, wherein each decision tree of the plurality of decisiontrees comprises unique attributes of the one or more first attributesrelative to other decision trees of the plurality of decision trees;determine, based on applying the second trained machine learning modelto values specific to the first consumer for the one or more firstattributes, a first prediction value that indicates a programmaticallyexpected number of future purchases by the first consumer; and transmit,to a first consumer device associated with the first consumer, one ormore promotions renderable for display by the first consumer device,wherein the one or more promotions are selected based at least in parton the first prediction value.
 2. The system of claim 1, wherein theidentified one or more first attributes comprise one or more attributesindicating one or more engagement scores for the first consumer.
 3. Thesystem of claim 2, wherein the identified one or more first attributescomprise one or more attributes corresponding to emails transferredbetween the first consumer and a promotional and marketing service. 4.The system of claim 1, wherein the computer memory apparatus in datacommunication with the data processing apparatus stores instructionsexecutable by the data processing apparatus and that upon such executionfurther cause the data processing apparatus to: receive data associatedwith a second consumer of the plurality of consumers, the secondconsumer being different from the first consumer; determine a secondclassification for the second consumer, wherein the secondclassification represents a second cohort of the plurality of cohorts towhich the second consumer belongs and is based on a measure of frequencyof purchases by the second consumer, and wherein the secondclassification of the second consumer is different from the firstclassification of the first consumer; identify one or more secondattributes based on attributes associated with the second cohort, theone or more second attributes being attributes selected for predictingthe respective one or more metric associated with the second consumer;determine, based on values specific to the second consumer for the oneor more second attributes and using a third trained machine learningmodel associated with the second cohort, a second prediction value thatindicates a programmatically expected number of purchases by the secondconsumer, wherein the second prediction value is different from thefirst prediction value; and transmit, to a second consumer deviceassociated with the second consumer, one or more promotions renderablefor display by the second consumer device, wherein the one or morepromotions are selected based at least in part on the second predictionvalue.
 5. The system of claim 4, wherein the computer memory apparatusin data communication with the data processing apparatus storesinstructions executable by the data processing apparatus and that uponsuch execution further cause the data processing apparatus to: determinea fourth prediction specifying an expected number of sales for a sellerduring a pre-specified period of time based, at least in part, on thefirst prediction and the second prediction.
 6. The system of claim 4,wherein the computer memory apparatus in data communication with thedata processing apparatus stores instructions executable by the dataprocessing apparatus and that upon such execution further cause the dataprocessing apparatus to: transmit a promotion for display to a firstconsumer device associated with the first consumer and not transmit apromotion for display to a second consumer device associated with thesecond consumer, based, at least in part, on the first prediction andthe second prediction.
 7. The system of claim 4, wherein the computermemory apparatus in data communication with the data processingapparatus stores instructions executable by the data processingapparatus and that upon such execution further cause the data processingapparatus to: transmit promotions to the first consumer at a differentrate from a rate at which promotions are transmitted to the secondconsumer, based, at least in part, on the first prediction and thesecond prediction.
 8. The system of claim 1, wherein the computer memoryapparatus in data communication with the data processing apparatusstores instructions executable by the data processing apparatus and thatupon such execution further cause the data processing apparatus to:apply a decay factor to the first prediction value, the first decayfactor being a factor that proportionally reduces the first predictionvalue, wherein the first decay factor is determined based on a measureof time lapsed between a time of the determination of the firstprediction value and a time when the decay factor is applied.
 9. Thesystem of claim 8, wherein the historical data comprises data associatedwith consumers classified with a same classification as the firstconsumer.
 10. The system of claim 1, wherein the computer memoryapparatus in data communication with the data processing apparatusstores instructions executable by the data processing apparatus and thatupon such execution further cause the data processing apparatus to:receive updated first values specific to the first consumer for the oneor more first attributes; determine that a first event associated withthe first consumer occurred; in response to determining that the firstevent occurred, determine based on the updated first values for the oneor more first attributes, an updated prediction value that indicates anupdated programmatically expected number of purchases by the firstconsumer; and transmit, to the first consumer device associated with thefirst consumer, one or more updated promotions renderable for display bythe first consumer device, wherein the one or more updated promotionsare selected based at least in part on the updated prediction value. 11.The system of claim 1, wherein the computer memory apparatus in datacommunication with the data processing apparatus stores instructionsexecutable by the data processing apparatus and that upon such executionfurther cause the data processing apparatus to: determine, based onvalues specific to the first consumer of third attributes different fromthe first attributes, a third prediction specifying whether the firstconsumer will make a purchase within a pre-specified time period,wherein the determining of the first prediction value is in response todetermining that third prediction indicates that the consumer will makea purchase within the pre-specified time period.
 12. The system of claim1, wherein one or more of the first trained machine learning model orsecond trained machine learning model comprises a random forest model.13. The system of claim 1, wherein the identified one or more firstattributes comprise one or more attributes corresponding to historicaldata associated with the first consumer.
 14. The system of claim 1,wherein the identified one or more first attributes comprise one or moreattributes corresponding to location data.
 15. The system of claim 1,wherein consumers that made a single purchase during a previous year areclassified as belonging to a one time buyer cohort, consumers that madetwo purchases during a previous year are classified as belonging to asporadic buyer cohort, consumers that made a minimum of three purchasesand a maximum of seven purchases during a previous year are classifiedas belonging to a loyal buyer cohort, and consumers that made eight ormore purchases during a previous year are classified as belonging to apower buyer cohort.
 16. The system of claim 1, wherein the one or moremetrics comprise one or more of a consumer lifetime value or a cohortrecall percentage.
 17. A system, comprising a data processing apparatusand a computer memory apparatus in data communication with the dataprocessing apparatus and storing instructions executable by the dataprocessing apparatus and that upon such execution cause the dataprocessing apparatus to: receive data associated with a first consumerof a plurality of consumers; determine a first classification for thefirst consumer, wherein the first classification represents a firstcohort of a plurality of cohorts to which the first consumer belongs andis based at least in part on a measure of frequency of historicalpurchases by the first consumer, wherein a set of attributes for eachcohort of the plurality of cohorts identified as having highest measuresof importance for predicting a given metric relative to other attributesfor each cohort is determined according to a first trained machinelearning model comprising a random forest model, wherein each decisiontree of the random forest model comprises unique attributes of theplurality of attributes relative to other decision trees of the randomforest model, and wherein the given metric is a prediction as to whetherthe first consumer will make a purchase within a pre-specified timeperiod; identify a set of attributes based on attributes associated withthe first cohort, the set of attributes being attributes selected, forthe first cohort, for predicting respective one or more metricassociated with consumers associated with the first cohort; select fromthe set of attributes a first subset of attributes and a second subsetof attributes, wherein the first subset of attributes is different fromthe second subset of attributes; select a second trained machinelearning model based on the first cohort, the second machine learningmodel comprising a plurality of decision trees generated based on theone or more first attributes associated with the first cohort, whereineach decision tree of the plurality of decision trees comprises uniqueattributes of the one or more first attributes relative to otherdecision trees of the plurality of decision trees; determine, based onapplying the second trained machine learning model to values specific tothe first consumer for the first subset of attributes, a firstprediction value that indicates whether the first consumer will make apurchase within a pre-specified time period; determine, based onapplying the second trained machine learning model to values specific tothe first consumer for the second subset of attributes, a secondprediction value that indicates whether the first consumer will make apurchase within a pre-specified time period; determine an overallprediction value based at least in part on the first and secondpredictions; and transmit, to a first consumer device associated withthe first consumer, one or more promotions renderable for display by thefirst consumer device, wherein the one or more promotions are selectedbased at least in part on the overall prediction value.
 18. The systemof claim 17, wherein the overall prediction value is a mode of the firstprediction value and second prediction value.
 19. The system of claim17, wherein the overall prediction value is further based on a thirdprediction value, the third prediction value being based on values for athird subset of attributes and indicating a programmatically expectednumber of purchases by the first consumer, wherein the third subset ofattributes is different from the first and the second subsets ofattributes.
 20. A system, comprising a data processing apparatus and acomputer memory apparatus in data communication with the data processingapparatus and storing instructions executable by the data processingapparatus and that upon such execution cause the data processingapparatus to: receive data associated with a first consumer of aplurality of consumers; determine a first classification for the firstconsumer, wherein the first classification represents a first cohort ofa plurality of cohorts to which the first consumer belongs and is basedat least in part on a measure of frequency of historical purchases bythe first consumer, wherein a set of attributes for each cohort of theplurality of cohorts identified as having highest measures of importancefor predicting a given metric relative to other attributes for eachcohort is determined according to a first trained machine learning modelcomprising a random forest model, wherein each decision tree of therandom forest model comprises unique attributes of the plurality ofattributes relative to other decision trees of the random forest model,and wherein the given metric is a prediction as to whether the firstconsumer will make a purchase within a pre-specified time period;identify a set of attributes based on attributes associated with thefirst cohort, the set of attributes being attributes selected, for thefirst cohort, for predicting respective one or more metric associatedwith consumers associated with the first cohort; select from the set ofattributes a first subset of attributes and a second subset ofattributes, wherein the first subset of attributes is different from thesecond subset of attributes; select a second trained machine learningmodel based on the first cohort, the second machine learning modelcomprising a plurality of decision trees generated based on the one ormore first attributes associated with the first cohort, wherein eachdecision tree of the plurality of decision trees comprises uniqueattributes of the one or more first attributes relative to otherdecision trees of the plurality of decision trees; determine, based onapplying the second trained machine learning model to values specific tothe first consumer for the first subset of attributes, a firstprediction value that indicates a programmatically expected number ofpurchases by the first consumer; determine, based on applying the secondtrained machine learning model to values specific to the first consumerfor the second subset of attributes, a second prediction value thatindicates a programmatically expected number of purchases by the firstconsumer; determine an overall prediction value based at least in parton the first and second predictions; and transmit, to a first consumerdevice associated with the first consumer, one or more promotionsrenderable for display by the first consumer device, wherein the one ormore promotions are selected based at least in part on the overallprediction value.